Rozprawy doktorskie: „AI DECISIONS”

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Blandford, Ann. "Design, decisions and dialogue". Thesis, Open University, 1991. http://oro.open.ac.uk/57316/.

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This thesis presents a design for an Intelligent Educational System to support the teaching of design evaluation in engineering. The design consists of a simple computerbased tool (or 'learning environment') for displaying and manipulating infonnation used in the course of problem solving, with a separate dialogue component capable of discussing aspects of the problem and of the problem solving strategy with the user. Many of the novel features of the design have been incorporated in a prototype system called WOMBAT. The main focus of this research has been on the design of the dialogue component. The design of the dialogue component is based on ideas taken from recent work on rational agency. The dialogue component has expertise in engaging in dialogues which support collaborative problem solving (involving system and user) in domains characterised as justified beliefs. It is capable of negotiating about what to do next and about what beliefs to take into account in problem solving. The system acquires problem-related beliefs by applying a simple plausible reasoning mechanism to a database of possible beliefs. The dialogue proceeds by turn-taking in which the current speaker constructs their chosen utterance (which may consist of several propositions and questions) and explicitly indicates when they have finished. When it is the system's turn to make an utterance, it decides what to say based on its beliefs about the current situation and on the likely utility of the various possible responses which it considers appropriate in the circumstances. Two aspects of the problem solving have been fully implemented. These are the discussion about what criteria a decision should be based on and the discussion about what decision step should be taken next. The system's contributions to the interaction are opportunistic, in the sense that at a dialogue level the system does not try to plan beyond the current utterance, and at a problem solving level it does not plan beyond the next action. The results of a formative evaluation of WOMBAT, in which it was exposed to a number of engineering educators, indicate that it is capable of engaging in a coherent dialogue, and that the dialogue is seen to have a pedagogical purpose. Although the approach of reasoning about the next action opportunistically has not proved adequate at a problem solving level, at a dialogue level it yields good results.

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Houtsma, Meile Jacob. "Perceived AI Performance and Intended Future Use in AI-based Applications". Thesis, Uppsala universitet, Institutionen för informatik och media, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414835.

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This case study explored perceived artificial intelligence (AI) performance and intended future use (IFU) in users of AI-based applications. Users could become less motivated to use these applications if AIs do not clearly communicate their actions. A prototype, a user test, and a structured interview were iteratively developed. Eight students participated in the final iteration, which was thematically analyzed. The results indicate that an AI-based application that shows recommendations can positively affect perceived AI performance and IFU. Possibly, the recommendations increased users’ understanding of AI decisions, as well as their satisfaction. Therefore, recommendations could be a potential design element for increasing perceived AI performance and IFU. Finally, time-saving functionality is a design element that could lead to higher IFU in AI-based applications, possibly only for other tasks than examining recommendations. Further research needs to test these findings under different circumstances.

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Ali, Kashan, i Kim Freimann. "Applying the Technology Acceptance Model to AI decisions in the Swedish Telecom Industry". Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21825.

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· Purpose Artificial Intelligence is one of the trend areas in research. It is applied in many different contexts successfully including Telecom sector. The purpose of this study is to replicate the study done in application of AI in the medical sector to understand the similar challenges of using AI in the Telecom sector. · Design/Methodology/approach Online questionnaire-based empirical study is used, and 190 responses were collected. First authors compare the general Technology acceptance model framework used in the medical sector and compare it with the non-AI users. Afterwards, this study proposes the improved TAM model that best fits into the Telecom sector. Later, this study uses the proposed improved model to compare the AI and non-AI users to understand the acceptance of AI-technology tools application in the Telecom sector. · Findings Confirmatory Factor analysis revealed that the general TAM model fit is adequate and applicable in Medical sector as well as in the Telecom sector. Also, hypothesis testing using SEM concluded that the general supported paths between the constructs and variables related to PU, PEU, SN, ATU, and BI in the medical sector is not same as in the Telecom sector. · Research limitations Results are based on the limited datasets from one of the larger companies in Telecom sector which could leads to inherent biases. Authors not sure if “AI-technology tools” in the questions have common understanding across all the respondents or not. · Results TAM model cannot be generalized across the sectors. An improved model has been developed used in the Telecom sector to analyze the user’s behavior and acceptance of AI technology. An extended model has been proposed which can be used as a continuation of this study. Keywords: Medical, Telecom, Artificial Intelligence, Network Intelligence, Technology acceptance model (TAM), Confirmatory Factor analysis (CFA), Structural equation modeling (SEM), Perceived usefulness (PU), Perceived Ease of Use (PEU), Subjective Norms (SN), Attitude Towards AI Use (ATU), Behavioural Intention (BI).

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Alabdallah, Abdallah. "Human Understandable Interpretation of Deep Neural Networks Decisions Using Generative Models". Thesis, Högskolan i Halmstad, Halmstad Embedded and Intelligent Systems Research (EIS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41035.

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Deep Neural Networks have long been considered black box systems, where their interpretability is a concern when applied in safety critical systems. In this work, a novel approach of interpreting the decisions of DNNs is proposed. The approach depends on exploiting generative models and the interpretability of their latent space. Three methods for ranking features are explored, two of which depend on sensitivity analysis, and the third one depends on Random Forest model. The Random Forest model was the most successful to rank the features, given its accuracy and inherent interpretability.

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Erhard, Annalena. "The Cost of Algorithmic decisions : A Systematic Literature Review". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85526.

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Decisions have been automated since the early days. Ever since the rise of AI, ML and DataAnalytics, algorithmic decision-making has experienced a boom time. Nowadays, using AI withina company is said to be critical to the success of a company. Considering the point that it can bequite costly to develop AI/ ML and integrating it into decision-making, it is striking how littleresearch was put into the identification and analysis of its cost drivers by now. This thesis is acontribution to raise and the awareness of possible cost drivers to algorithmic decisions. Thetopic was divided in two subgroups. That is solely algorithms and hybrid decision-making. Asystematic literature review was conducted to create a theoretical base for further research. Thecost drivers for algorithms to make decisions without human interaction, the identified costdrivers identified can be found at Data Storage (including initial, floor rent, energy, service,disposal, and environmental costs), Data Processing, Transferring and Migrating. Additionally,social costs and the ones related to fairness as well as the ones related to algorithms themselves(Implementation and Design, Execution and Maintenance) could be found. Business Intelligenceused for decision making raises costs in Data quality, Update delays of cloud systems, Personneland Personnel training, Hardware, Software, Maintenance and Data Storage. Moreover, it isimportant to say that the recurrence of some costs was detected. Further research should go inthe direction of applicability of the theoretical costs in practice.

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MIGLIERINA, ENRICO CARLO. "UN APPROCCIO DINAMICO AI PROBLEMI DI OTTIMIZZAZIONE VETTORIALE". Doctoral thesis, Università degli studi di Trieste, 2002. http://thesis2.sba.units.it/store/handle/item/13246.

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Orefors, Emil, i Nouri Issaki. "AI IN CONTEXT BASED STATISTICS IN CLINICAL DECISION SUPPORT". Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-39923.

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Some treatments may cause unwanted effects and may make it difficult to achieve an optimal personalised decision for a specific patient. Decision support systems in healthcare is a topic that is getting much attention today. The purpose of using such a system is to enhance treatment's quality and to make it easier for clinicians to process and providing information by having access to patient's electronic health record and past experience. In this thesis, the developed a Clinical decision support system (CDSS) that helps clinicians to identify similar patients and extracting relevant experience. The vision here is to enable clinicians to make more informed decisions when choosing a suitable treatment for patient’s condition. So, here we focus on a more generic approach using case-based reasoning (CBR) and clustering in order to enable context-based statistics for a wider usage of CDSS in healthcare. We are testing our framework on a specific register that considers patients with cerebral pares and their ability to walk. In addition, the solution in our framework will measure how much the range of motions during the foot changes (increase or decrease) before and after an operation of the patient. During this work, an interview has been conducted with a clinical expert to collect requirements to develop such systems. The main function of the system is to check if a patient is similar to any previous patients so the clinician can get relevant information in choosing better treatment solution for a patient. The clinician involved in the project was convinced that our approach could become a valuable tool in a clinical decision-making situation.

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Elbegzaya, Temuulen <1991&gt. "Application AI in Traditional Supply Chain Management Decision-Making". Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17733.

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In the generation of demand uncertainty and complex market, the ability to fully integrate and orchestrate the entire supply chain spectrum of end-to-end processes from acquiring materials to converting, to delivery to final customers is highly desired by many organizations. While data sourcing, managing and manipulating are becoming one of the core advantages in the businesses, a number of leading-edge organizations have been studying and exploring the limits of machine learning and artificial intelligence (AI) to enrich excellence. The common usage of AI is being referr toed in extensive computational modelling for reasoning, recognizing patterns, calculating endless possibilities, learning, and understanding from the experience to facilitate one's needs. Especially in demand planning and forecasting, AI and/or machine learning is being used to guide effective planning of future demands with industrial precision of 85%, but lacks in full implementation among other sub-applications in supply chain (SC) such as MRP, MPS, predictive maintenance, and learning from experience instantly. One area of AI’s potential application that has not fully explored is in emerging management philosophy of SCM that requires the comprehension of complex interactions, real-time joint problem solving, and interrelated decision-making processes. This absence of competency in AI is due to lack of replicating information input on practical implications, technical merits, problem scopes, complex heuristics and long-term analysis that human brain can perform. With this obstacle in mind, this paper will concentrate one following hypotheses: - Identification of sub-application problems in SCM that can be solved through AI and machine learning algorithms - Exploring other literature and exploratory works on AI development and designs in SCM - Summarizing modern SCM models that can be addressed and replicated in AI application areas, problem scopes and methodology - Discuss and develop wproblem-solvingtraditional manager’s decision-makithe ng process in SCM using AI/ML techniques - Examine and synthesize SC data inputs required to enhance technical integrity and joint problem-solving in AI - Review future outlook on multitude of application of AI and machine learning in SCM

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Frank, Michael Patrick. "Advances in decision-theoretic AI : limited rationality and abstract search". Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/34070.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (p. 153-165).
by Michael Patrick Frank.
M.S.

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Latora, Antonio Giuseppe. "Metodologie Analytic Hierarchy Process ibride per applicazioni di Multiple Criteria Decision Analysis ai processi di Procurement". Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1040.

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Costi e benefici connessi alle decisioni che quotidianamente caratterizzano la vita dell uomo, discendono dai molteplici e spesso contrastanti punti di vista o criteri utilizzati nell attività di decision making divenuta oggetto di approfonditi e recenti studi afferenti ad una vera e propria disciplina matematica definita Multiple Criteria Decision Analysis. Ogni decisione non rappresenta esclusivamente un atto di scelta bensì il risultato di un processo decisionale di comprensione e modellazione del problema ovverosia una serie di attività che consentono la trasformazione del problema in soluzione determinando nel decision maker un passaggio di stato psicologico da una percezione di insoddisfazione ad una di soddisfazione. Gli studi realizzati in ambito MCDA hanno originato due differenti scuole di pensiero: la scuola francese basata principalmente sul concetto di outranking e la scuola americana delle Multi - Attribute Utility and Value Theories; a quest ultima appartiene l Analytic Hierarchy Process, metodologia MCDA sviluppata da Thomas Lorie Saaty al fine operare il ranking di un numero definito o indefinito di alternative; il confronto a coppie, ovverosia la misurazione dell importanza relativa tra potenziali azioni o alternative, secondo un criterio o punto di vista di livello superiore, consente infatti la determinazione di una scala di priorità per entità intangibili, per definizione prive di scale di misura, ma anche per entità tangibili valutabili in topologia metrica su idonee scale di misura. Obiettivo della tesi di dottorato è stato lo studio, finalizzato alla modifica e all applicazione, della metodologia MCDA AHP al procurement, processo di ingegneria gestionale dedicato all approvvigionamento di beni e servizi nel contesto di una generica value chain. L e-procurement è una realtà consolidata nei contesti business-to-consumer, business-to-business e business-to-government, ma il modello concernente il generico processo di approvvigionamento, dato l odierno stato dell arte dell ICT applicata al procurement, non può considerarsi standard di riferimento poiché elaborato in un epoca caratterizzata da sistemi informativi ed informatici indubbiamente differenti dagli attuali riguardo a tecnologie e schemi concettuali. La metodologia Hybrid Analytic Hierarchy Process oggetto di ricerca, ha tratto origine dalla definizione di Saaty secondo la quale un numero non ha alcun significato se non quello assegnato ad esso da chi è chiamato ad interpretarlo . In un contesto caratterizzato da grandezze quali - quantitative, le metodologie ibride H-AHP-R ed H-AHP-A si caratterizzano poiché consentono il ranking parallelo e seriale di un numero definito o indefinito di alternative mediante lean MCDA ovverosia mediante calcolo diretto NO-AHP del rating ideale con riferimento ai criteri quantitativi e mediante calcolo AHP-R ed AHP-A del rating ideale riferito ai criteri qualitativi. Disegnando un processo di lean e scouting basato su metodologia MCDA H-AHP-A, lo studio eseguito, relativamente a specifiche tipologie di acquisti, ha re-ingegnerizzato parzialmente il generico processo di approvvigionamento, con l obiettivo di rendere razionale, efficiente, efficace e conforme ai criteri di valutazione individuati, la scelta dell alternativa di approvvigionamento nell universo delle soluzioni individuate sul web, al fine di massimizzare il valore dell acquisto e minimizzare i tempi di scelta.

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Aldeano, João Pedro Candeias Coxinho Tomé. "A decision support system in shuttle service managing". Master's thesis, Universidade de Évora, 2020. http://hdl.handle.net/10174/27923.

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The transport sector is an important field in it is worth investing time to find new solutions aimed at making vehicle routes more efficient in terms of time and petrol consumption. The solution presented in this dissertation tries to help a small shuttle company to manage their shuttles, drivers and customer requests, but could be generalised as a solution to a bigger shuttle company. The implemented system uses Artificial Intelligence techniques by representing the drivers attribution as a constraint satisfaction optimisation problem. Drivers have a start location, start times and vehicle capacity. Service constraints are the pick up and drop off locations, times, and service weight (weight of baggage and passengers). All these constraints generate a branch of the well known vehicle routing problem by adding time windows, capacity and multiple depot constraints. The system uses constraint optimisation tools that aim to return an optimised schedule given a list of shuttle company drivers and shuttle services; SUMÀRIO: Sistema de suporte à decisão na gestão de serviços de tranfers O sector dos transportes é bastante importante e vale a pena investir tempo para encontrar novas soluções com o objectivo de fazer o escalonamento de veículos mais eficiente, em termos de tempo e também consumo de combustíveis. A solução apresentada nesta dissertação, tenta ajudar uma pequena companhia de transferes a gerir a sua frota, condutores e pedidos de clientes, mas pode ser generalizada sendo uma solução para companhias de transferes maiores. O sistema implementado utiliza técnicas de inteligência artificial para representar a atribuição de serviços aos condutores como um problema de satisfação de restrições. Os condutores têm como atributos uma localização inicial, um tempo de começo de serviço e a capacidade do veiculo como restrições. Os serviços têm como restrições o ponto de partida e o ponto de chegada, o tempo de partida e também um peso associado, isto é, o peso de bagagem e número de passageiros. Todas estas restrições geram um ramo do bem conhecido problema do caixeiroviajante adicionando restrições: de peso, janelas de tempo e também de múltiplos depósitos. O sistema utiliza ferramentas de optimização de restrições, no qual o objectivo é retornar um escalonamento optimizado, dada uma lista de condutores e uma lista de serviços de transferes.

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WIEHLER, Lukas. "How can AI regulation be effectively enforced? : comparing compliance mechanisms for AI regulation with a multiple-criteria decision analysis". Doctoral thesis, European University Institute, 2022. http://hdl.handle.net/1814/74805.

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Award date: 17 June 2022. Supervisor: Professor Andrea Renda (European University Institute)
Newly emerging AI regulations need effective and innovative enforcement and compliance mechanisms to assure that fundamental and human rights are protected when using an AI system. This study compares four different compliance mechanisms namely ‘Real-Time and Automated Conformity Assessment’, ‘Standardization and Certification’, ‘Algorithmic Impact Assessment’ and ‘Algorithmic Auditing’ as well as three different assurers of compliance namely deployers, notified bodies and civil society organisations. With an MCDA, this research has shown that civil society-based compliance mechanisms are believed to be less effective, less feasible and more costly compared to all other compliance mechanisms. Second, external compliance mechanisms (by notified bodies) were rated to be more effective but also more difficult to implement compared to internal compliance mechanisms. Third, algorithmic auditing scored highest among all policy options. Fourth, despite its experimental nature, automated and real-time compliance mechanisms are not scored significantly lower than other compliance mechanisms.

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Yusuf, Syed Adnan. "An evolutionary AI-based decision support system for urban regeneration planning". Thesis, University of Wolverhampton, 2010. http://hdl.handle.net/2436/114896.

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The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.

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Nygren, Fredrik, i Olof Thelander. "Modernt beslutsfattande, Människa eller AI : En kvalitativ studie om hur olika faktorer påverkar investerares beslutsfattande kring investeringar i AI-styrda fonder". Thesis, Linköpings universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160543.

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Bakgrund: Det har länge forskats kring beslutsfattande och i centrum av forskningen finns människans oförmåga att fatta rationella beslut. I modern tid har AI fått en allt större betydelse och det har uppdagats att AI har möjligheten att överkomma människans oförmåga att fatta rationella beslut. I takt med digitaliseringen har AI och dess förmåga att hantera stora mängder information blivit ett användbart verktyg på investeringsmarknaden. De AI-styrda fonderna öppnar för ännu ett investeringsalternativ och det råder i dagsläget brist på förståelse för hur investerares beslut om att inkludera dessa fonder i sitt sparande påverkas av beteendemässiga faktorer. Syfte: Syftet med uppsatsen är att skapa förståelse för hur ett beslut om att investera i AI-styrda fonder påverkas av investerares beteendemässiga faktorer och den AI-styrda fondens egenskaper. Genomförande: Uppsatsen är en kvalitativ små-N-studie med ett hermeneutiskt perspektiv. Det empiriska materialet har samlats in genom ett bekvämlighets- och ett målstyrt urval. Sammanlagt 18 semi-strukturerade intervjuer har genomförts. Slutsats: Uppsatsen skapar förståelse för hur ett beslut om att investera i AI-styrda fonder påverkas av investerares beteendemässiga faktorer och den AI-styrda fondens egenskaper. Investerares känslor för och associationer till AI påverkar den initiala inställningen och används för att utvärdera för- och nackdelar. Den kunskap och det intresse investerare har för sparande och investeringar påverkar till vilken grad för- och nackdelar övervägs. De med högre respektive lägre kunskap ser olika värden i den AI-styrda fondens egenskaper. Beroende på dessa beteendemässiga faktorer ställer sig investerare olika till huruvida rationalitet eller intuition efterfrågas vid investeringsbeslut.
Background: Decision making has been researched for a long time and in the center of the research is the human inability to make rational decisions. In modern time, AI has become increasingly important and it has been discovered that AI has the opportunity to overcome the human inability to make rational decisions. In line with digitalization, AI and its ability to managed large amount of information has become a useful tool on the investment market. The AI-managed funds open for yet another investment alternative and there is currently a lack of understanding of how investors’ decision to include these funds in their savings are influenced by behavioral factors. Purpose: The purpose of this study is to create an understanding of how a decision to invest in AI-managed funds is affected by investors’ behavioral factors and attributes of the AI-managed fund. Completion: This study is a qualitative small-N-study with a hermeneutic perspective. The empirical data has been gathered through a target and a convenience sample. A total of 18 semi structured interviews have been conducted. Conclusion: The study contributes to an increased understanding of how different factors in the investor and how the attributes of the AI-managed fund affect the investor's decision. Investors' feelings for and associations with AI affect the initial attitude and are used to evaluate the pros and cons. The extent to which the advantages and disadvantages of the alternatives are considered is largely determined by the knowledge and interest the investors have for savings and investments, which results in those with higher and lower knowledge seeing different values in the attributes of the AI-managed fund. Depending on these factors, investors differ as to whether rationality or intuition is required in investment decisions.

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Wang, Ming-hua. "A knowledge-based system approach for project management decision-making support". Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340476.

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Schiele, Julian [Verfasser], i Jens O. [Akademischer Betreuer] Brunner. "AI-Enabled Decision Support in Health Care / Julian Schiele ; Betreuer: Jens O. Brunner". Augsburg : Universität Augsburg, 2020. http://d-nb.info/1217194029/34.

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Tersander, Jacob. "AI – Can You Afford To Wait?" Thesis, KTH, Skolan för industriell teknik och management (ITM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241051.

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The paradigm of diffusion research can be traced back all the way to the 1940s when Ryan and Gross investigated the diffusion of hybrid seed among farmers in Iowa. Since the 1960s diffusion research has been applied in a wide variety of disciplines, for instance, to study the diffusion of the Internet and the non-diffusion of the Dvorak keyboard. Currently, the technologies that are on top of the Gartner Hype Cycle are all associated with Artificial Intelligence (AI), which shortly can be defined as learning devices that perceive their environment and take actions to maximize their success at some goal. Consequently, some people suggest that the current hype surrounding AI can be the end of the human kind, while others believe it will give way for millions of fresh jobs and cleverer decision-making. In recent years both media and political organizations have shown great interest in AI. In addition, the industry is captivated by the potential uses of AI. In the last years, AI-related companies in the US have raised billions of dollars in the stock market together with a large number of acquisitions. The large flow of capital into AI technology underpins the fast development of AI solutions. The purpose of this study is to investigate how groups approach AI. What can be concluded after reviewing different sectors is that organizations seem to share a common interest of AI. Furthermore, organizations share the opinion that eventually AI will be a more natural part of their processes. Organizations investing a larger share of their budget in R&D have a longer experience of using AI and are currently doing projects utilizing more advanced technologies within AI. In organizations from other sectors, the investments in AI depend on the people with the authority to invest money in projects and their view on AI. Organizations generally seem to approach AI in a similar way. Firstly, they evaluate what AI is. Secondly, they find areas to make small iterative PoC-projects utilizing AI, usually with machine learning. Finally, more money is invested if the PoC-projects were successful and the organization starts looking at how to acquire more competence within the area to fully exploit the value of AI.
Paradigmet för innovationsspridning kan spåras ända tillbaka till 1940-talet när Ryan och Gross undersökte spridningen av hybridfrön bland bönder i Iowa. Sedan 1960-talet har forskningen tillämpats inom en mängd olika discipliner, till exempel för att studera spridningen av Internet och icke-spridningen av Dvorak-tangentbordet. För närvarande är teknologierna som ligger på toppen av Gartner Hype-cykeln alla förknippade med artificiell intelligens (AI), som kan definieras som lärande enheter som uppfattar sin miljö och vidtar åtgärder för att maximera sin framgång gällande något mål. Hypen som nu finns kring AI har lett till att vissa människor tror att det kan innebära slutet för mänskligheten medan andra tror att det kommer att ge plats för miljoner nya jobb och smartare beslutsfattande. Under de senaste åren har både medier och politiska organisationer visat stort intresse för AI samt visat intresse för potentiella användningsområden av AI. AI-relaterade företag i USA har under de senaste åren har tagit in miljarder dollar i riskkapital. Ett stort antal förvärv och kapitalflödet till AI-teknik ökar den snabba utvecklingen av AI-lösningar. Syftet med denna studie är att beskriva spridningen av AI i organisationer från ett antal olika sektorer. Vad som kan sägas efter att ha studerat olika sektorer är att organisationer delar en gemensam nyfikenhet för AI och att de tror att AI kommer bli en allt mer naturlig del av sina processer. De företag som spenderar mycket pengar på FoU har längre erfarenhet av att använda AI och gör för närvarande projekt som använder mer avancerade tekniker. I andra organisationer är investeringarna inom AI beroende av de anställda som har rätt att investera pengar i projekt och deras syn på AI. Organisationer verkar allmänt närma sig AI på ett liknande sätt där de först utvärderar vad AI är. Därefter väljer de ett antal områden där de gör små iterativa projekt där de utnyttjar AI, vanligtvis via ML. Därefter investerades mer pengar om de små projekten lyckas och företaget börjar titta på hur man kan förvärva mer kompetens inom området.

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Mugrauer, Alex, i Johannes Pers. "Marketing managers in the age of AI : A multiple-case study of B2C firms". Thesis, Umeå universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161285.

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The growing capacity of artificial intelligence (AI) has been compared to how electricity transformed our world and industries a hundred years ago. AI is changing the rules, roles and tools of marketing, as marketing is one of the most prosperous areas to implement AI in. Accordingly, the role of marketing manager is expected to transform to a large extent. This calls for further research, since the area appears underexplored in relation to its weight of importance. Thus, the main purpose of this thesis is to contribute to the literature in marketing and management by exploring the ongoing transformation of the role of marketing managers. In order to fulfill this purpose, the following research question was formulated: How is the role of marketing managers in B2C-firms affected by increased levels of AI-capacity? In order to answer this question, a qualitative multiple-case study was conducted through semi-structured interviews with three marketing managers with experience of AI-usage in their field, as well as three experts at the intersection of AI and marketing. This triangulation was conducted to attain a nuanced and complementary view of the investigated area. The findings of this study suggest that increased levels of AI-capacity affect the role of marketing managers in B2C-firms both externally and internally. First, the increased usage of AI-technologies allows for better financial accountability of marketing managers, thus marketing managers can avert a greater influence in top management. Increased AI-capacity further implicates marketing in the way that marketing becomes more relevant, as well as opening new possibilities to build stronger relationships with customers. Further implicating the role of marketing managers are the evolving AI-assistants, in which the findings of this study suggest that they will increasingly become a crucial distribution channel and consequently, brands will increase in importance. This stands in contrast to previous research, which suggests as the AI-assistant solely base their decision on facts; focus should be shifted towards optimizing value propositions towards the AI-assistants, instead of the consumer. The findings of this study suggest that optimization towards the AI-assistants will indeed be crucial. But as more firms do so, the competitive advantage will be reduced to a hygiene factor, since all actors optimize in the same way, as was the case when search engine optimization grew and saturated. Thus, brand building will once again become a highly important decision-criterion among consumers. Further, as customer data collection is a major component in the usage of AI-technologies in marketing, the findings of this study suggest marketing managers will increasingly work with establishing transparent policies that benefit the customers. Moreover, increased usage of AItechnologies further demands marketing managers to be sufficiently knowledgeable in technology. As the responding experts believe AI-technologies are unlikely to replicate human EQ, intuition and creativity for a foreseeable future, marketing managers will still be vital to firms. This because the responding marketing managers reported these capabilities to be highly crucial in their role.

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Eriksson, Falk Filiph, i Fredrik Frenning. "Intelligent Matching For Clinical Decision Support System For Cerebral Palsy Using Domain Knowledge". Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-36231.

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Relevant information at the right time can be critically important for clinicians when treating patients with cerebral palsy (CP). Gathering this information could be done through the usage of a clinical decision support system with a matching algorithm that finds relevant patients. The relevancy of this information for clinicians is determined by the relevancy of the matched patients. The aim of this thesis was therefore to investigate how an algorithm that matches similar patients with CP could be improved in terms of relevancy. The goal was also to explore the possibilities of domain knowledge and temporal aspects and how they could be combined and utilized in order to improve the matching algorithm. In this bachelor's thesis, we have conducted a literature study about the domain and a domain knowledge survey. The domain knowledge survey included gathering domain knowledge through contact with an expert in the area of CP. We also implemented an algorithm using intelligent similarity measurements based on validation from experts that could accurately match similar patients according to the domain knowledge gathered. The resulting algorithm is presented through a prototype of a CDSS, which allows clinicians to select and match patients through a GUI, and including features such as adjusting weight values for different attributes. The algorithm uses patient data retrieved from the CPUP database, which is specfic to patients with CP, to match with. From the CPUP database many temporal aspects could be concluded to be relevant for similarity assessment. Due to the limited scope of the thesis however, only the most important aspect was utilized. By treating this aspect as an attribute like the other domain knowledge based attributes, but with respect to other variables that affected it, a combination of temporal aspects and domain knowledge was done when identifying similar patients with CP. Using the prototype of the CDSS with the implemented algorithm could help clinicians make better informed decisions, and this leads to improved health care for children and patients with CP, which is why this thesis was important.

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VETTORETTO, LUCIANO. "Nuovi sviluppi della ricerca territoriale in relazione ai problemi attuali di conoscenza e decisione : teorie, metodo, esperienze". Doctoral thesis, Università IUAV di Venezia, 1987. http://hdl.handle.net/11578/278088.

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ADORNI, ROBERTA. "Dinamiche elettrofisiologiche nella lettura di parole: dall'analisi ortografica ai processi di elaborazione semantica". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7832.

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The purposes of the doctoral thesis were manifold. First of all, we aimed at clarifying the neural underpinnings of single word reading and the time course of lexical processing by comparing the bioelectrical activity elicited by stimuli with different degrees of orthographic legality (letter strings, legal pseudo-words and words) and semantic denotation (flora names vs. fauna names; concrete words vs. abstract words). A strong effort was directed to avoid possible confounding effects of psycholinguistic variables like frequency of occurrence and length, which are known to affect written word processing. Source reconstruction by means of LORETA was aimed to draw inferences on the cerebral regions specifically involved in different processing stages, with the final purpose of integrate current knowledge resulting from neuroimaging and electrophysiological approach. Overall the data provided evidence that the orthographic and lexico-semantic word properties were processed in parallel between 200 and 400 ms after stimulus onset. The temporo-parietal region was sensitive to orthographic legality (250-350 ms). The latency of lexical effects (word/pseudo-word discrimination) varied as a function of the number of a word's orthographic neighbours, being faster (200-250 ms) to pseudo-words with a small number of neighbours over anterior sites. The lexical effect found over the left occipito-temporal region (300-400 ms, BA 19 and BA 37) suggested a lexical access by means of a visual route. Semantic categories were discriminated as early as 200 ms post stimulus over the left occipito-temporal areas (BA 37 and BA 20). The results are discussed in the light of the possible role played by the word age of acquisition, as well as the specific properties of living concepts (biological relevance and homomorphism). Abstract and concrete word processing differed in terms of a stronger involvement of extra-striate visual areas during concrete word processing (350-380 ms) and a stronger involvement of prefrontal cortex in response to abstract words (350-380 ms). The results suggested that concrete, imaginable concepts activate perceptually based representations not available to abstract concepts. Overall, these data show how ERPs can dissociate between lexical and higher level processes. Semantic processing may take place near-simultaneously and in different brain regions with the processing of information about the form of a word and its lexical properties.

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ALBÅGE, PETTERSSON ANNIE, i HOLMES KLARA ANDERHAGEN. "Beslutsfattande kring produktutvecklingsprocessen i svenska klädföretag och framtida AI-applikationer : En studie om svenska klädföretags hållbara material- och produktbeslut samt AI-stöd i framtiden". Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279761.

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I klädindustrin är materialval och beslut om produkter en viktig del av produktutvecklingsprocessen som påverkar design, kostnad, känsla samt estetik i en produkt. Materialvalsprocessen har blivit mer komplex och utseende samt kvalitet konkurrerar nu också med hållbarhet. Många aspekter är därför viktiga att balansera vid beslutsfattande. Det finns metoder, men om och hur de används av svenska klädföretag är relativt outforskat. Artificiella intelligenta-system som kan användas i beslutsfattandet finns tillgängliga idag och används redan i olika områden inom klädindustrin. Det finns också AI-system för materialval, men ännu inte i klädindustrin. Syftet med denna studie är att studera beslutsfattande inom svenska klädföretag avseende hållbara produkt- och materialval samt om AI skulle kunna stödja processen. Semistrukturerade intervjuer genomförda med två svenska klädföretag användes i denna studie för att besvara två forskningsfrågor kopplade till syftet tillsammans med en iterativ litteraturstudie. Studien visar att svenska klädföretag inte använder några speciella verktyg eller kända metoder vid beslutstagande gällande material och produkter för att uppnå deras hållbarhetsmål. Ofta är det intuition och erfarenhet som ligger bakom beslut och val men det finns även kravspecifikationer för varje produkt med krav som måste vara uppfyllda för att beslutet om materialval ska tas. I dag använder företagen inga AI-system men enligt teorin finns det några AI-metoder som skulle kunna implementeras inom material- och produktbeslut där det gäller kvantitativa aspekter, exempelvis vattentäthet. Dock kan inte något AI-system i dag ersätta människans förmåga att ta beslut vad gäller mer kvalitativa egenskaper, såsom känslan av ett material.
In the apparel industry, material choice and decisions regarding products are an important part of the product development process, influencing design, cost, feeling and aesthetic of a product. The material choice process is becoming more complex and look along with quality now also competes with sustainability. Many aspects are therefore important to balance when making decisions. There are methods, but it is unclear if and how they are used by Swedish apparel companies. Artificial Intelligence systems that can be used in decision making are available today, and already in use in different areas within the apparel industry. There are also AI-systems for material choice, but not yet in the apparel industry. The purpose of this study is to study decision making within Swedish clothing companies regarding sustainable product and material choices and whether AI could support the process. Semi structured interviews conducted with two Swedish apparel companies were used to answer two research questions, together with an iterative literature study. The study shows that Swedish apparel companies do not use any special tools or known methods while making decisions regarding material and products in order to achieve their sustainability goals. Often intuition and experience is behind making a decision or choice, but also specifications regarding each product with requirements that have to be fulfilled. Today the asked companies do not use AI-systems for making decisions regarding materials or products, but according to literature, there are some methods that possibly could be implemented regarding quantitative aspects such as water permeability. However, there does not seem to be any AI-systems today that can replace human’s ability to make decisions concerning the qualitative aspects such as the feeling of a material.

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Sottara, Davide <1981&gt. "Integration of symbolic and connectionist AI techniques in the development of Decision Support Systems applied to biochemical processes". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amsdottorato.unibo.it/2972/1/Sottara_Davide_Tesi.pdf.

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Sottara, Davide <1981&gt. "Integration of symbolic and connectionist AI techniques in the development of Decision Support Systems applied to biochemical processes". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amsdottorato.unibo.it/2972/.

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Wang, Olivier. "Adaptive Rules Model : Statistical Learning for Rule-Based Systems". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX037/document.

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Les Règles Métiers (Business Rules en anglais, ou BRs) sont un outil communément utilisé dans l’industrie pour automatiser des prises de décisions répétitives. Le problème de l’adaptation de bases de règles existantes à un environnement en constante évolution est celui qui motive cette thèse. Des techniques existantes d’Apprentissage Automatique Supervisé peuvent être utilisées lorsque cette adaptation se fait en toute connaissance de la décision correcte à prendre en toute circonstance. En revanche, il n’existe actuellement aucun algorithme, qu’il soit théorique ou pratique, qui puisse résoudre ce problème lorsque l’information connue est de nature statistique, comme c’est le cas pour une banque qui souhaite contrôler la proportion de demandes de prêt que son service de décision automatique fait passer à des experts humains. Nous étudions spécifiquement le problème d’apprentissage qui a pour objectif d’ajuster les BRs de façon à ce que les décisions prises aient une valeur moyenne donnée.Pour ce faire, nous considérons les bases de Règles Métiers en tant que programmes. Après avoir formalisé quelques définitions et notations dans le Chapitre 2, le langage de programmation BR ainsi défini est étudié dans le Chapitre 4, qui prouve qu’il n’existe pas d’algorithme pour apprendre des Règles Métiers avec un objectif statistique dans le cas général. Nous limitons ensuite le champ d’étude à deux cas communs où les BRs sont limités d’une certaine façon : le cas Borné en Itérations dans lequel, quelles que soit les données d’entrée, le nombre de règles exécutées en prenant la décision est inférieur à une borne donnée ; et le cas Linéaire Borné en Itérations dans lequel les règles sont de plus écrite sous forme Linéaire. Dans ces deux cas, nous produisons par la suite un algorithme d’apprentissage basé sur la Programmation Mathématique qui peut résoudre ce problème. Nous étendons brièvement cette formalisation et cet algorithme à d’autres problèmes d’apprentissage à objectif statistique dans le Chapitre 5, avant de présenter les résultats expérimentaux de cette thèse dans le Chapitre 6
Business Rules (BRs) are a commonly used tool in industry for the automation of repetitive decisions. The emerging problem of adapting existing sets of BRs to an ever-changing environment is the motivation for this thesis. Existing Supervised Machine Learning techniques can be used when the adaptation is done knowing in detail which is the correct decision for each circumstance. However, there is currently no algorithm, theoretical or practical, which can solve this problem when the known information is statistical in nature, as is the case for a bank wishing to control the proportion of loan requests its automated decision service forwards to human experts. We study the specific learning problem where the aim is to adjust the BRs so that the decisions are close to a given average value.To do so, we consider sets of Business Rules as programs. After formalizing some definitions and notations in Chapter 2, the BR programming language defined this way is studied in Chapter 3, which proves that there exists no algorithm to learn Business Rules with a statistical goal in the general case. We then restrain the scope to two common cases where BRs are limited in some way: the Iteration Bounded case in which no matter the input, the number of rules executed when taking the decision is less than a given bound; and the Linear Iteration Bounded case in which rules are also all written in Linear form. In those two cases, we later produce a learning algorithm based on Mathematical Programming which can solve this problem. We briefly extend this theory and algorithm to other statistical goal learning problems in Chapter 5, before presenting the experimental results of this thesis in Chapter 6. The last includes a proof of concept to automate the main part of the learning algorithm which does not consist in solving a Mathematical Programming problem, as well as some experimental evidence of the computational complexity of the algorithm

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Dellaluce, Jason. "Enhancing symbolic AI ecosystems with Probabilistic Logic Programming: a Kotlin multi-platform case study". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23856/.

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As Artificial Intelligence (AI) progressively conquers the software industry at a fast pace, the demand for more transparent and pervasive technologies increases accordingly. In this scenario, novel approaches to Logic Programming (LP) and symbolic AI have the potential to satisfy the requirements of modern software environments. However, traditional logic-based approaches often fail to match present-day planning and learning workflows, which natively deal with uncertainty. Accordingly, Probabilistic Logic Programming (PLP) is emerging as a modern research field that investigates the combination of LP with the probability theory. Although research efforts at the state of the art demonstrate encouraging results, they are usually either developed as proof of concepts or bound to specific platforms, often having inconvenient constraints. In this dissertation, we introduce an elastic and platform-agnostic approach to PLP aimed to surpass the usability and portability limitations of current proposals. We design our solution as an extension of the 2P-Kt symbolic AI ecosystem, thus endorsing the mission of the project and inheriting its multi-platform and multi-paradigm nature. Additionally, our proposal comprehends an object-oriented and pure-Kotlin library for manipulating Binary Decision Diagrams (BDDs), which are notoriously relevant in the context of probabilistic computation. As a Kotlin multi-platform architecture, our BDD module aims to surpass the usability constraints of existing packages, which typically rely on low level C/C++ bindings for performance reasons. Overall, our project explores novel directions towards more usable, portable, and accessible PLP technologies, which we expect to grow in popularity both in the research community and in the software industry over the next few years.

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Björklund, Pernilla. "The curious case of artificial intelligence : An analysis of the relationship between the EU medical device regulations and algorithmic decision systems used within the medical domain". Thesis, Uppsala universitet, Juridiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442122.

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The healthcare sector has become a key area for the development and application of new technology and, not least, Artificial Intelligence (AI). New reports are constantly being published about how this algorithm-based technology supports or performs various medical tasks. These illustrates the rapid development of AI that is taking place within healthcare and how algorithms are increasingly involved in systems and medical devices designed to support medical decision-making. The digital revolution and the advancement of AI technologies represent a step change in the way healthcare may be delivered, medical services coordinated and well-being supported. It could allow for easier and faster communication, earlier and more accurate diagnosing and better healthcare at lower costs. However, systems and devices relying on AI differs significantly from other, traditional, medical devices. AI algorithms are – by nature – complex and partly unpredictable. Additionally, varying levels of opacity has made it hard, sometimes impossible, to interpret and explain recommendations or decisions made by or with support from algorithmic decision systems. These characteristics of AI technology raise important technological, practical, ethical and regulatory issues. The objective of this thesis is to analyse the relationship between the EU regulation on medical devices (MDR) and algorithmic decision systems (ADS) used within the medical domain. The principal question is whether the MDR is enough to guarantee safe and robust ADS within the European healthcare sector or if complementary (or completely different) regulation is necessary. In essence, it will be argued that (i) while ADS are heavily reliant on the quality and representativeness of underlying datasets, there are no requirements with regard to the quality or composition of these datasets in the MDR, (ii) while it is believed that ADS will lead to historically unprecedented changes in healthcare , the regulation lacks guidance on how to manage novel risks and hazards, unique to ADS, and that (iii) as increasingly autonomous systems continue to challenge the existing perceptions of how safety and performance is best maintained, new mechanisms (for transparency, human control and accountability) must be incorporated in the systems. It will also be found that the ability of ADS to change after market certification, will eventually necessitate radical changes in the current regulation and a new regulatory paradigm might be needed.

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Hammarström, Tobias. "Towards Explainable Decision-making Strategies of Deep Convolutional Neural Networks : An exploration into explainable AI and potential applications within cancer detection". Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424779.

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The influence of Artificial Intelligence (AI) on society is increasing, with applications in highly sensitive and complicated areas. Examples include using Deep Convolutional Neural Networks within healthcare for diagnosing cancer. However, the inner workings of such models are often unknown, limiting the much-needed trust in the models. To combat this, Explainable AI (XAI) methods aim to provide explanations of the models' decision-making. Two such methods, Spectral Relevance Analysis (SpRAy) and Testing with Concept Activation Methods (TCAV), were evaluated on a deep learning model classifying cat and dog images that contained introduced artificial noise. The task was to assess the methods' capabilities to explain the importance of the introduced noise for the learnt model. The task was constructed as an exploratory step, with the future aim of using the methods on models diagnosing oral cancer. In addition to using the TCAV method as introduced by its authors, this study also utilizes the CAV-sensitivity to introduce and perform a sensitivity magnitude analysis. Both methods proved useful in discerning between the model’s two decision-making strategies based on either the animal or the noise. However, greater insight into the intricacies of said strategies is desired. Additionally, the methods provided a deeper understanding of the model’s learning, as the model did not seem to properly distinguish between the noise and the animal conceptually. The methods thus accentuated the limitations of the model, thereby increasing our trust in its abilities. In conclusion, the methods show promise regarding the task of detecting visually distinctive noise in images, which could extend to other distinctive features present in more complex problems. Consequently, more research should be conducted on applying these methods on more complex areas with specialized models and tasks, e.g. oral cancer.

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Forslund, Lia, i Mentzer Sofia von. "Sjukvårdskris och svalt mottagande av AI, hur går det ihop? : En fallstudie i vilka faktorer som har störst påverkan på införandet av artificiell intelligens". Thesis, Uppsala universitet, Institutionen för informatik och media, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414559.

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Det svenska sjukvårdssystemet är konstant under hög press och situationen benämns ofta i media som en sjukvårdskris. Radiologin är en av de medicinska discipliner som drabbats av en kontinuerligt ökande arbetsbelastning och personalbrist. Detta sätter sjukvården i en situation att konstant tvingas väga effektivitet mot kvalitet. Trots höga förväntningar på att innovationer som Artificiell Intelligens (AI) ska kunna bistå behoven, används AI idag i en mycket begränsad utsträckning. Denna studie syftar till att utreda påverkande faktorer för införandet av AI inom radiologin. För att besvara arbetets forskningsfråga har HA Adoption-Decision Model, en modifierad version av det väletablerade Technology-Organization-Environment Framework (TOE), tillämpats. Ramverket innefattar tre kontexter; teknologisk, organisatorisk och extern kontext. Varje kontexts delaspekter, så kallade faktorer, följer under respektive kontext. Dessa tio faktorer utvärderades för att besvara studiens forskningsfråga om vilka faktorer som har störst påverkan på införande av AI inom radiologi. Genom att förena tidigare forskning med resultatet från sex intervjuer visade sig affärsvärde , strategisk lämplighet , ledningsstöd och reglering av datahantering ha störst påverkan. Avslutningsvis presenteras ett förslag om att introducera en elfte faktor, IT-mognad, till ramverket.

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Cooper, Tessa L. "Case Adaptation for an Intelligent Decision Support System for Diabetes Management". Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1289585163.

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Wang, Brydon. "The role of trustworthiness in automated decision-making systems and the law". Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/231388/1/Brydon_Wang_Thesis.pdf.

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This thesis considers the role of trustworthiness in automated decision-making systems (ADS) spanning across data collection, modelling, analysis and decision output in different legal contexts. Through an updated model of trustworthiness, it argues that existing legal norms and principles for administering construction contracts and the impact of automation on these contracts provide fertile ground to inform the governance of algorithmic systems in smart cities. The thesis finds that trustworthy, benevolent ADS requires a specific form of transparency that operates through mutual vulnerability in the trusting relationship, and seams in the automated decision-making process where human discretion is exercised.

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Berggren, Andreas, Martin Gunnarsson i Johannes Wallin. "Artificial intelligence as a decision support system in property development and facility management". Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-25535.

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The construction industry has been hesitant for a long time to apply new technologies. In property development, the industry relies heavily on employees bringing experience from one project to another. These employees learn to manage risks in connection with the acquisition of land, but when these people retire, the knowledge disappears. An AI-based decision-support system that takes the risks and the market into account when acquiring land can learn from each project and bring this knowledge into future projects. In facility management, artificial intelligence could increase the efficiency of the allocation of staff in the ongoing operations. The purpose of the study is to analyse how companies in the real estate industry can improve their decision-making with the help of AI in property development and property management. In this study, two case studies of two different players in the real estate industry have been performed. One player, Bygg-Fast, represents property development and the other player, VGR, represents facility management. The study is based on interviews, discussions, and collected data. By mapping and then quantifying the risks and market indicators that are input data in the process, a basis can be created. The data can be used for a model that lays the foundation for an AI-based decision support system that will help the property developer to make calculated decisions in the land acquisition process. By mapping what a flow through a property looks like, measuring points can be set out to analyse how long the activities take in the specific business. These measured values provide a collection of data that makes it easier to plan the activities conducted in the property. A more efficient flow can be achieved by visualizing the entire process so staff can be allocated to the right part of the flow. By being flexible and being able to re-plan the business quickly if planning is disrupted, a high level of efficiency can be achieved. This could be done by an AI-based decision support system that simulates alternative day plans.
Byggbranschen har länge varit tveksamt till att applicera nya tekniker. Inom fastighetsutveckling bygger branschen mycket på att anställda tar med sig erfarenheter från ett projekt till ett annat. Dessa anställda lär sig hantera risker i samband med förvärv av mark men när dessa personer slutar eller går i pension försvinner kunskapen. Ett AI baserat beslutssystem som tar risk och marknad i beaktning vid förvärv av mark kan lära sig av varje projekt och ta med dessa kunskaper till framtida projekt. Inom fastighetsförvaltning skulle artificiell intelligens kunna effektivisera allokerandet av personal i den pågående verksamheten. Syftet med studien är att analysera hur företag i fastighetsbranschen kan förbättra sitt beslutstagande med hjälp av AI i utveckling av fastigheter samt fastighetsförvaltning. I denna studien har två fallstudier av två olika aktörer i fastighetsbranschen utförts. Ena aktören, Bygg-Fast, representerar fastighetsutveckling och den andra aktören, VGR, representerar fastighetsförvaltning. Studien bygger på intervjuer, diskussioner och insamlade data. Genom att kartlägga och sedan kvantifiera de risker samt marknadsindikatorer som är indata i processen kan ett underlag skapas. Underlaget kan användas för en modell som lägger grunden för ett AI baserat beslutsstödsystem som ska hjälpa fastighetsutvecklaren med att ta kalkylerade beslut i mark förvärvsprocessen. Genom att kartlägga hur ett flöde genom en fastighet ser ut kan mätpunkter sättas ut för att analysera hur lång tid aktiviteterna tar i den specifika verksamheten. Dessa mätvärden ger en samlad data som gör det lättare att planera verksamheten som bedrivs i fastigheten. Ett effektivare flöde kan uppnås genom att visualisera hela processen så personal kan allokeras till rätt del av flödet. Genom att vara flexibel och kunna planera om verksamheten snabbt ifall planering störs kan en hög effektivitet nås. Detta skulle kunna göras av ett AI baserat beslutsstödsystem som simulerar alternativa dagsplaneringar.

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Bergquist, Olsson Frida, i Hanna Dahl. "Co-creators or puppets? : a study on AI-marketing’s role in consumers’ value co-creation". Thesis, Högskolan Kristianstad, Fakulteten för ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-22155.

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In recent years, AI has received increased attention in the field of marketing and is believed to grow even more in the future. It seems that the use of AI in marketing has a significant impact on consumer value creation. The purpose of this thesis is to explore how consumers experience the role of AI-marketing within the scope of the online customer journey, including how both positive and negative aspects of AI-based decision aids influence their value co-creation. Based on previous research, a conceptual model was created to determine whether consumers co-create or co-destroy value in the interactions with AI-based decision aids, as well as which types of values that are the outcomes. Three focus groups were used as a qualitative method to collect empirical data. Findings resulted in two main insights. Firstly, consumers experience the role of AI-based decision aids positively and can co-create values regarding economic and efficiency benefits, facilitation of information search, and more inspiring, personal and relevant experiences. Secondly, AI-based decision aids are also perceived negatively and contribute to co-destruction of value as well. Consumers experienced negative aspects regarding manipulation, limitations and loss of integrity and autonomy, as well as risks of overspending, being misled and distracted, and having irrelevant experiences. This thesis contributes with new insights into the consumer perspective of AI-marketing. Marketers can use these results to understand consumers’ value creation and avoid the negative aspects to achieve the best possible AI-marketing strategies.

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Walker, Donald. "Similarity Determination and Case Retrieval in an Intelligent Decision Support System for Diabetes Management". Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1194562654.

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Haviland, Hannah. ""The Machine Made Me Do It!" : An Exploration of Ascribing Agency and Responsibility to Decision Support Systems". Thesis, Linköping University, Centre for Applied Ethics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2922.

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Are agency and responsibility solely ascribable to humans? The advent of artificial intelligence (AI), including the development of so-called “affective computing,” appears to be chipping away at the traditional building blocks of moral agency and responsibility. Spurred by the realization that fully autonomous, self-aware, even rational and emotionally-intelligent computer systems may emerge in the future, professionals in engineering and computer science have historically been the most vocal to warn of the ways in which such systems may alter our understanding of computer ethics. Despite the increasing attention of many philosophers and ethicists to the development of AI, there continues to exist a fair amount of conceptual muddiness on the conditions for assigning agency and responsibility to such systems, from both an ethical and a legal perspective. Moral and legal philosophies may overlap to a high degree, but are neither interchangeable nor identical. This paper attempts to clarify the actual and hypothetical ethical and legal situations governing a very particular type of advanced, or “intelligent,” computer system: medical decision support systems (MDSS) that feature AI in their system design. While it is well-recognized that MDSS can be categorized by type and function, further categorization of their mediating effects on users and patients is needed in order to even begin ascribing some level of moral or legal responsibility. I conclude that various doctrines of Anglo legal systems appear to allow for the possibility of assigning specific types of agency – and thus specific types of legal responsibility – to some types of MDSS. Strong arguments for assigning moral agency and responsibility are still lacking, however.

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Klingvall, Emelie. "Artificiell intelligens som ett beslutsstöd inom mammografi : En kvalitativ studie om radiologers perspektiv på icke-tekniska utmaningar". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18768.

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Artificiell intelligence (AI) har blivit vanligare att använda för att stödja människor i deras beslutsfattande. Maskininlärning (ML) är ett delområde inom AI som har börjat användas mer inom hälso-och sjukvården. Patientdata ökar inom vården och ett AI-system kan behandla denna ökade datamängd, vilket vidare kan utveckla ett beslutsstöd som hjälper läkarna. AI-tekniken blir vanligare att använda inom radiologin och specifikt inom mammografin som ett beslutsstöd. Användning av AI-teknik inom mammografin medför fördelar men det finns även utmaningar som inte har något med tekniken att göra.Icke-tekniska utmaningar är viktiga att se över för att generera en lyckad praxis. Studiens syfte var därför att undersöka icke-tekniska utmaningar vid användning av AI som ett beslutsstöd inom mammografi ur ett radiologiskt perspektiv. Radiologer med erfarenhet av mammografi intervjuades i syfte att öka kunskapen kring deras syn på användningen.Resultatet från studien identifierade och utvecklade de icke-tekniska utmaningarna utifrån temana: ansvar, mänskliga förmågor, acceptans, utbildning/kunskap och samarbete. Resultatet indikerade även på att inom dessa teman finns icke-tekniska utmaningar med tillhörande aspekter som är mer framträdande än andra. Studien ökar kunskaperna kring radiologers syn på användningen och bidrar till framtida forskning för samtliga berörda aktörer. Forskning kan ta hänsyn till dessa icke-tekniska utmaningar redan innan tekniken är implementerad i syfte att minska risken för komplikationer.
Artificial intelligence (AI) has become more commonly used to support people when making decisions. Machine learning (ML) is a sub-area of AI that has become more frequently used in health care. Patient data is increasing in healthcare and an AI system can help to process this increased amount of data, which further can develop a decision support that can help doctors. AI technology is becoming more common to use in radiology and specifically in mammography, as a decision support. The usage of AI technology in mammography has many benefits, but there are also challenges that are not connected to technology.Non-technical challenges are important to consider and review in order to generate a successful practice. The purpose of this thesis is therefore to review non-technical challenges when using AI as a decision support in mammography from a radiological perspective. Radiologists with experience in mammography were interviewed in order to increase knowledge about their views on the usage.The results identified and developed the non-technical challenges based on themes: responsibility, human abilities, acceptance, education/knowledge and collaboration. The study also found indications within these themes that there are non-technical challenges with associated aspects that are more prominent than others. This study emphasizes and increases the knowledge of radiologists views on the usage of AI and contributes to future research for all the actors involved. Future research can address these non-technical challenges even before the technology is implemented to reduce the risk of complications.

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Beka, Be Nguema Marius. "Comportement de l'opérateur humain face à une situation dégradée et imprévue : contribution à la réalisation d'une interface homme-machine tolérante à certaines erreurs humaines". Valenciennes, 1994. https://ged.uphf.fr/nuxeo/site/esupversions/3e915137-f166-4513-adfd-f97943c83baf.

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La complexité de la tâche d'un opérateur humain et la variabilité de sa charge de travail, dans un système homme machine, peuvent le conduire à commettre des erreurs aux conséquences graves. Aussi, l'impossibilité de les éliminer dans leur totalité rend nécessaire la présence d'une interface tolérante à ces erreurs, capable d'empêcher leurs conséquences sur le système homme-machine. Le thème de cette recherche consiste donc à analyser le comportement de l'opérateur humain en surcharge mentale ou face à une dégradation plus ou moins importante des informations d'un procédé simule simple de réglage de température d'eau. L’analyse du comportement des quarante-quatre sujets testés a permis par la suite l'extraction de règles générales à introduire dans la spécification de l'interface tolérante aux erreurs humaines. La modélisation de l'opérateur humain qui constitue une étape importante vers la conception de cette interface a été écrite en logique floue. Cette interface est composée de différents modules tels que la classification de l'action de l'opérateur, la résolution de l'action, et des modèles de l'opérateur humain et du procédé. La validation de la structure d'interface proposée tout comme les résultats de l'analyse des comportements des opérateurs humains ont permis d'énoncer des recommandations générales pour la conduite assistée de procédés.

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Lindstam, Tim, i Anton Svensson. "Behavior Based Artificial Intelligence in a Village Environment". Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20522.

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Abstract. Autonomous agents, also known as AI agents, are staples in modern video games. They take a lot of roles, everything from being quest-givers in roleplaying games, to opposing forces in action- and shooter games. Crafting an AI that is not only easy to create, but also retains humanlike and believable behavior, has always represented a challenge to the development industry, and has in several cases ended up with open world games using AI systems that limit the AI agents to simple moving patterns. In this thesis, a form of AI systems more commonly used in simulation games such as The Sims video game series, are taken and implemented in an environment that could possibly be seen in an open world game. After the implementation, a set of tests were performed on a group of testers which resulted in the insight that a majority of the testers, when asked to compare their experience to other games, found this implementation to feel more lifelike and realistic.

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Fredriksson, Tomas, i Rickard Svensson. "Analysis of machine learning for human motion pattern recognition on embedded devices". Thesis, KTH, Mekatronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246087.

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With an increased amount of connected devices and the recent surge of artificial intelligence, the two technologies need more attention to fully bloom as a useful tool for creating new and exciting products. As machine learning traditionally is implemented on computers and online servers this thesis explores the possibility to extend machine learning to an embedded environment. This evaluation of existing machine learning in embedded systems with limited processing capa-bilities has been carried out in the specific context of an application involving classification of basic human movements. Previous research and implementations indicate that it is possible with some limitations, this thesis aims to answer which hardware limitation is affecting clas-sification and what classification accuracy the system can reach on an embedded device. The tests included human motion data from an existing dataset and included four different machine learning algorithms on three devices. Support Vector Machine (SVM) are found to be performing best com-pared to CART, Random Forest and AdaBoost. It reached a classification accuracy of 84,69% between six different included motions with a clas-sification time of 16,88 ms per classification on a Cortex M4 processor. This is the same classification accuracy as the one obtained on the host computer with more computational capabilities. Other hardware and machine learning algorithm combinations had a slight decrease in clas-sification accuracy and an increase in classification time. Conclusions could be drawn that memory on the embedded device affect which al-gorithms could be run and the complexity of data that can be extracted in form of features. Processing speed is mostly affecting classification time. Additionally the performance of the machine learning system is connected to the type of data that is to be observed, which means that the performance of different setups differ depending on the use case.
Antalet uppkopplade enheter ökar och det senaste uppsvinget av ar-tificiell intelligens driver forskningen framåt till att kombinera de två teknologierna för att både förbättra existerande produkter och utveckla nya. Maskininlärning är traditionellt sett implementerat på kraftfulla system så därför undersöker den här masteruppsatsen potentialen i att utvidga maskininlärning till att köras på inbyggda system. Den här undersökningen av existerande maskinlärningsalgoritmer, implemen-terade på begränsad hårdvara, har utförts med fokus på att klassificera grundläggande mänskliga rörelser. Tidigare forskning och implemen-tation visar på att det ska vara möjligt med vissa begränsningar. Den här uppsatsen vill svara på vilken hårvarubegränsning som påverkar klassificering mest samt vilken klassificeringsgrad systemet kan nå på den begränsande hårdvaran. Testerna inkluderade mänsklig rörelsedata från ett existerande dataset och inkluderade fyra olika maskininlärningsalgoritmer på tre olika system. SVM presterade bäst i jämförelse med CART, Random Forest och AdaBoost. Den nådde en klassifikationsgrad på 84,69% på de sex inkluderade rörelsetyperna med en klassifikationstid på 16,88 ms per klassificering på en Cortex M processor. Detta är samma klassifikations-grad som en vanlig persondator når med betydligt mer beräknings-resurserresurser. Andra hårdvaru- och algoritm-kombinationer visar en liten minskning i klassificeringsgrad och ökning i klassificeringstid. Slutsatser kan dras att minnet på det inbyggda systemet påverkar vilka algoritmer som kunde köras samt komplexiteten i datan som kunde extraheras i form av attribut (features). Processeringshastighet påverkar mest klassificeringstid. Slutligen är prestandan för maskininlärningsy-stemet bunden till typen av data som ska klassificeras, vilket betyder att olika uppsättningar av algoritmer och hårdvara påverkar prestandan olika beroende på användningsområde.

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Lemaitre, Juliette. "Vers une simplification de la conception de comportements stratégiques pour les opposants dans les jeux vidéo de stratégie". Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2343/document.

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Cette thèse aborde la problématique de la création d’intelligences artificielles (IA) contrôlant la prise de décision haut-niveau dans les jeux de stratégie. Ce type de jeux propose des environnements complexes nécessitant de manipuler de nombreuses ressources en faisant des choix d’actions dépendant d’objectifs à long terme. La conception de ces IA n’est pas simple car il s’agit de fournir une expérience pour le joueur qui soit divertissante et intéressante à jouer. Ainsi, le but n’est pas d’obtenir des comportements d’IA imbattables, mais plutôt de refléter différents traits de personnalités permettant au joueur d’être confronté à des adversaires diversifiés. Leur conception fait intervenir des game designers qui vont définir les différentes stratégies en fonction de l’expérience qu’ils souhaitent créer pour le joueur, et des développeurs qui programment et intègrent ces stratégies au jeu. La collaboration entre eux nécessite de nombreux échanges et itérations de développement pour obtenir un résultat qui correspond aux attentes des designers. L’objectif de cette thèse est de proposer une solution de modélisation de stratégies accessible aux game designers en vue d’améliorer et de simplifier la création de comportements stratégiques. Notre proposition prend la forme d’un moteur stratégique choisissant des objectifs à long terme et vient se placer au dessus d’un module tactique qui gère l’application concrète de ces objectifs. La solution proposée n’impose pas de méthode pour résoudre ces objectifs et laisse libre le fonctionnement du module tactique. Le moteur est couplé à un modèle de stratégie permettant à l’utilisateur d’exprimer des règles permettant au moteur de choisir les objectifs et de leur allouer des ressources. Ces règles permettent d’exprimer le choix d’objectifs en fonction du contexte, mais également d’en choisir plusieurs en parallèle et de leur donner des importances relatives afin d’influencer la répartition des ressources. Pour améliorer l’intelligibilité nous utilisons un modèle graphique inspiré des machines à états finis et des behavior trees. Les stratégies créées à l’aide de notre modèle sont ensuite exécutées par le moteur de stratégie pour produire des directives qui sont données au module tactique. Ces directives se présentent sous la forme d’objectifs stratégiques et de ressources qui leur sont allouées en fonction de leurs besoins et de l’importance relative qui leur a été donnée. Le module stratégique permet donc de rendre accessible la conception du niveau stratégique d’une IA contrôlant un adversaire dans un jeu de stratégie
This PhD thesis addresses the topic of creating artificial intelligence (AI) to control high-level decision-making in strategy games. This kind of game offers complex environments that require the manipulation of a large number of resources by choosing actions depending on long-term goals. This AI design is not simple because it is about providing to the player a playful and interesting experience. Hence, the aim is not to create unbeatable behaviors, but rather to display several personality traits allowing the player to face diverse opponents. Its creation involves game designers who are responsible of defining several strategies according to the experience they want to provide to the player, and game developers who implement those strategies to put them into the game. The collaboration between them requires many exchanges and development iterations to obtain a result corresponding to game designers’ expectations. The objective of this PhD thesis is to improve and simplify the creation of strategical behaviors by proposing a strategy model intelligible to game designers and that can be interfaced easily with developers’ work. For game designers, a strategy model has been created to allow them to express rules guiding the choice of goals and their allocated resources. These rules make it possible for game designers to express which goal to choose according to the context but also to choose several of them and give them relative importance in order to influence the resource distribution. To improve intelligibility we use a graphical model inspired from finite state machines and behavior trees. Our proposition also includes a strategy engine which executes the strategies created with the model. This execution produces directives that are represented by a list of selected strategical goals and the resources that have been allocated according to the importance and needs of each goal. These directives are intended for a tactical module in charge of their application. The developers are then responsible for the implementation of this tactical module. Our solution enables game designers to directly design the strategical level of an AI and therefore facilitates their cooperation with game developers and simplifies the entire creation process of the AI

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Birindwa, Fleury. "Prestandajämförelse mellan Xception, InceptionV3 och MobileNetV2 för bildklassificering på nätpaneler". Thesis, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-51351.

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Under de senaste året har modeller för djupinlärning använts inom nästa alla områden, från industri till akademi, särskilt för bildklassifikation. Dessa modeller är dock enorma i storlek, med miljontals parametrar, vilket gör det svårt att distribuera till mindre enheter med begränsade resurser såsom mobiltelefoner. Denna studie tar upp små modeller av faltningsnätverk som är toppmoderna inom djupinlärning och vars storlek är lämplig för mobilapplikation. Syftet med denna studie är att utvärdera prestanda på faltningsnätverken Xception, InceptionV3 och MobilNetV2 för att underlätta vid valbeslut av faltningsnätverk som bas vid utveckling av mobila applikation inom bildklassificering. För att uppnå syftet har dessa faltningsnätverk implementeras med hjälp av överföringsinlärning metod samt utformas för att skilja på bilder av nätpaneler från företaget Troax. Studien tar upp metoden som möjliggör att överföra kunskap från befintliga förtränade modeller till nya modeller. Studien förklarar även hur träningsprocessen och testprocessen gick till samt analys kring resultatet. Resultat visade att Xception hade 86 % noggrannhet med en processtid på 10 minuter på 2000 träningsbilder och 1000st testbilder. Xceptions prestation var bäst bland alla dessa modeller. Skillnaden mellan Xception och Inception var på 10 % noggrannhet och 2 minuter processtid. Mellan Xception och MobilNetV2 var skillnaden på 23 % noggrannhet och 3 minuter processtid. Experimentet visade att dessa modeller presterade mindre bra vid mindre träningsbilder under 800st. Över 800st bilder började respektive modell att utföra prediktering över 70 % noggrannhet.
In recent years, deep learning models have been used in almost all areas, from industry to academia, specifically for image classification. However, these models are huge in size, with millions of parameters, making it difficult to distribute to smaller devices with limited resources such as mobile phones. This study addresses lightweight pre-trained models of convolutional neural networks which is state of art in deep learning and their size is suitable as a base model for mobile application development. The purpose of this study is to evaluate the performance of Xception, InceptionV3 and MobilNetV2 in order to facilitate selection decisions of a lightweight convolutional networks as base for the development of mobile applications in image classification. In order to achieve their purpose, these models have been implemented using the Transfer Learning method and are designed to distinguish images on mesh panels from the company Troax. The study takes up the method that allows transfer of knowledge from an existing model to a new model, explain how the training process and the test process went, as well as analysis of results. Results showed that Xception had 86% accuracy and had 10 minutes processing time on 2000 training images and 1000 test images. Exception’s performance was the best among all these models. The difference between Xception and InceptionV3 was 10% accuracy and 2 minutes process time. Between Xception and MobilNetV2 there was a difference of 23% in accuracy and 3 minutes in process time. Experiments showed that these models performed less well with smaller training images below 800 images. Over 800 images, each model began to perform prediction over 70% accuracy.

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Kolář, Vít. "Umělá inteligence ve hře Bang!" Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-235543.

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The goal of this master's thesis is to create an artificial intelligence for the Bang! game. There is a full description of the Bang! game, it's entire rules, player's using strategy principles and game analysis from UI point of view included. The thesis also resumes methods of the artificial intelligence and summarizes basic information about the domain of game theory. Next part describes way of the implementation in C++ language and it's proceeding with use of Bayes classification and decision trees based on expert systems. Last part represent analysis of altogether positive results and the conclusion with possible further extensions.

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Bjarnolf, Philip. "Threat Analysis Using Goal-Oriented Action Planning : Planning in the Light of Information Fusion". Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1108.

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An entity capable of assessing its and others action capabilities possess the power to predict how the involved entities may change their world. Through this knowledge and higher level of situation awareness, the assessing entity may choose the actions that have the most suitable effect, resulting in that entity’s desired world state.

This thesis covers aspects and concepts of an arbitrary planning system and presents a threat analyzer architecture built on the novel planning system Goal-Oriented Action Planning (GOAP). This planning system has been suggested for an application for improved missile route planning and targeting, as well as being applied in contemporary computer games such as F.E.A.R. – First Encounter Assault Recon and S.T.A.L.K.E.R.: Shadow of Chernobyl. The GOAP architecture realized in this project is utilized by two agents that perform action planning to reach their desired world states. One of the agents employs a modified GOAP planner used as a threat analyzer in order to determine what threat level the adversary agent constitutes. This project does also introduce a conceptual schema of a general planning system that considers orders, doctrine and style; as well as a schema depicting an agent system using a blackboard in conjunction with the OODA-loop.

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Giannini, Valentina <1969&gt. "Knowledge sharing among and within stakeholder groups to cope with climate related risks". Doctoral thesis, Università Ca' Foscari Venezia, 2012. http://hdl.handle.net/10579/1170.

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Methods to operationalize climate change adaptation are explored by developing tools for knowledge integration within participatory processes. Two paradigms are taken into consideration, and case studies are developed in relation to them. The first paradigm is Integrated Water Resources Management and the case study used is the research project BRAHMATWINN. The second paradigm is Disaster Risk Reduction (DRR) and the case study is identified based upon a knowledge harmonization process taking place in Guatemala. Three are the main results: 1. the Integrated Indicator Table useful to establish a biunivocal relation between research outcomes and stakeholders’ needs; 2. the Gap Analysis Matrix useful to identify governance and policy gaps in the law and its implementation with respect to flood risk; 3. the Total Cognitive Map useful to collect and analyse visions stakeholders have on risk and DRR, and to identify and improve possible synergies among institutions and organizations dealing with DRR.
Sono sviluppati metodi per rendere operativo l’adattamento ai cambiamenti climatici a partire da processi partecipativi per l’armonizzazione del sapere. Il primo paradigma affrontato è la gestione integrata delle risorse idriche, caso studio è il progetto BRAHMATWINN; il secondo paradigma è la gestione del rischio, con un caso studio fondato su un progetto di armonizzazione delle conoscenze in corso in Guatemala. Tre sono i risultati: 1. la tabella integrata degli indicatori, mediante la quale si è stabilita una relazione biunivoca fra risultati della ricerca e necessità degli attori locali; 2. la matrice per l’analisi delle carenze di politiche per il rischio inondazione, in cui sono identificati ritardi nella legislazione e nella sua implementazione; 3. la mappa cognitiva totale, attraverso cui si possono raccogliere ed analizzare le visioni che gli attori locali hanno sul rischio e sulla sua gestione, per identificare e migliorare le sinergie possibili fra istituzioni che si occupano di gestione del rischio.

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Knapek, Petr. "Řízení entit ve strategické hře založené na multiagentních systémech". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403185.

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This thesis is focused on designing and implementing system, that adds learning and planning capabilities to agents designed for playing real-time strategy games like StarCraft. It will explain problems of controlling game entities and bots by computer and introduce some often used solutions. Based on analysis, a new system has been designed and implemented. It uses multi-agent systems to control the game, utilizes machine learning methods and is capable of overcoming oponents and adapting to new challenges.

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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries". Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.

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Benjamin, Michael R. "The Interval Programming Model for Multi-objective Decision Making". 2004. http://hdl.handle.net/1721.1/30416.

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The interval programming model (IvP) is a mathematical programmingmodel for representing and solving multi-objective optimizationproblems. The central characteristic of the model is the use ofpiecewise linearly defined objective functions and a solution methodthat searches through the combination space of pieces rather thanthrough the actual decision space. The piecewise functions typicallyrepresent an approximation of some underlying function, but thisconcession is balanced on the positive side by relative freedom fromfunction form assumptions as well as the assurance of global optimality.In this paper the model and solution algorithms are described, and theapplicability of IvP to certain applications arediscussed.

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Martin, Martin C. "The Essential Dynamics Algorithm: Essential Results". 2003. http://hdl.handle.net/1721.1/6718.

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This paper presents a novel algorithm for learning in a class of stochastic Markov decision processes (MDPs) with continuous state and action spaces that trades speed for accuracy. A transform of the stochastic MDP into a deterministic one is presented which captures the essence of the original dynamics, in a sense made precise. In this transformed MDP, the calculation of values is greatly simplified. The online algorithm estimates the model of the transformed MDP and simultaneously does policy search against it. Bounds on the error of this approximation are proven, and experimental results in a bicycle riding domain are presented. The algorithm learns near optimal policies in orders of magnitude fewer interactions with the stochastic MDP, using less domain knowledge. All code used in the experiments is available on the project's web site.

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Wu, Chin-Hui, i 吳智暉. "University-level Automated Course Scheduling by Integrating AI Technique and Group Decision Support System - the Preceding Process". Thesis, 1994. http://ndltd.ncl.edu.tw/handle/25388860988100308858.

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碩士
大葉大學
電機工程研究所
82
Computing university course schedules is very hard. Course scheduling is basically a multiple constraint satisfaction problem, in which the determination of a solution is NP- complete. The approaches oriented to operations research simplified the problem to facilitate mathematical model building and to reduce computation time. The AI/expert-system- oriented approaches took advantage of powerful configuration tools and supplied reasoning methods, but did not completely solve the conflict problem between multiple constraints. Via literature review and system analysis, this research proposes simple heuristic rules to guide ''generate, test and debug'' strategy to automate ng. A prototype system has been developed, tested and evaluated. Course-scheduling by heuristic rules can reduce computation time significantly. And the huristic rules themselves are easier to understand than mathematical models.

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Lay, Young-Jinn, i 賴永進. "University-level Automated Course Scheduling by Integrating AI Technique and Group Decision Support System - Group Negotiation Timetabling". Thesis, 1994. http://ndltd.ncl.edu.tw/handle/88277612855266149559.

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碩士
大葉大學
電機工程研究所
82
University-level course scheduling is basically a multiple constraint satisfaction problem. It needs to rely on a preceding process to get a feasible solution satisfactory to almost all constraints and on a nogotiation process to achieve a all- satisfying solution. Researches in autometed course scheduling proposed various algorithms, empirical rules and reasoning thods. Proposals were differentiated by computation time and memory space usage, but they were not guaranteed to succeed in finding a solution. The final stage in course scheduling is achieved by negotiation, precisely, a group decision process. This research proposes a course-specific group decision support system to ease the inherent negotiation activities required for the course scheduling issues. A course- specific group decision support system needs some major functions as information query, group negotiation, course adjustment, course scheduling, explanation, constraint relaxation and system help. A prototype system under this general architecture has been developed, tested and evaluated. The testing and the evaluation of the system has gained positive public opinions.

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