Dissertations / Theses on the topic 'Artificial Intelligence, Explainable AI'
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Vincenzi, Leonardo. "eXplainable Artificial Intelligence User Experience: contesto e stato dell’arte." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23338/.
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Il grande sviluppo del mondo dell’Intelligenza Artificiale unito alla sua vastissima applicazione in molteplici ambiti degli ultimi anni, ha portato a una sempre maggior richiesta di spiegabilità dei sistemi di Machine Learning. A seguito di questa necessità il campo dell’eXplainable Artificial Intelligence ha compiuto passi importanti verso la creazione di sistemi e metodi per rendere i sistemi intelligenti sempre più trasparenti e in un futuro prossimo, per garantire sempre più equità e sicurezza nelle decisioni prese dall’AI, si prevede una sempre più rigida regolamentazione verso la sua spiegabilità.
Per compiere un ulteriore salto di qualità, il recente campo di studio XAI UX si pone come obiettivo principale l’inserimento degli utenti al centro dei processi di progettazione di sistemi di AI, attraverso la combinazione di tecniche di spiegabilità offerte dall'eXplainable AI insieme allo studio di soluzioni UX.
Il nuovo focus sull’utente e la necessità di creare team multidisciplinari, e quindi con maggiori barriere comunicative tra le persone, impongono ancora un largo studio sia da parte degli esperti XAI sia da parte della comunità HCI, e sono attualmente le principali difficoltà da risolvere.
All’interno dell’elaborato si fornisce una visione attuale sullo stato dell'arte della XAI UX introducendo le motivazioni, il contesto e i vari ambiti di ricerca che comprende.
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Gjeka, Mario. "Uno strumento per le spiegazioni di sistemi di Explainable Artificial Intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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L'obiettivo di questa tesi è quello di mostrare l’importanza delle spiegazioni in un sistema intelligente. Il bisogno di avere un'intelligenza artificiale spiegabile e trasparente sta crescendo notevolmente, esigenza evidenziata dalla ricerca delle aziende di sviluppare sistemi informatici intelligenti trasparenti e spiegabili.
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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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Costa, Bueno Vicente. "Fuzzy Horn clauses in artificial intelligence: a study of free models, and applications in art painting style categorization." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673374.
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Aquesta tesi doctoral contribueix a l’estudi de les clàusules de Horn en lògiques difuses, així com al seu ús en representació difusa del coneixement aplicada al disseny d’un algorisme de classificació de pintures segons el seu estil artístic. En la primera part del treball ens centrem en algunes nocions rellevants per a la programació lògica, com ho són per exemple els models lliures i les estructures de Herbrand en lògica matemàtica difusa. Així doncs, provem l’existència de models lliures en classes universals difuses de Horn, i demostrem que tota teoria difusa universal de Horn sense igualtat té un model de Herbrand. A més, introduïm dues nocions de minimalitat per a models lliures, i demostrem que aquestes nocions són equivalents en el cas de les fully named structures. En la segona part de la tesi doctoral, utilitzem les clàusules de Horn combinades amb el modelatge qualitatiu com a marc de representació difusa del coneixement per a la categorització d’estils de pintura artística. Finalment, dissenyem un classificador de pintures basat en clàusules de Horn avaluades, descriptors qualitatius de colors i explicacions. Aquest algorisme, anomenat l-SHE, proporciona raons dels resultats obtinguts i mostra percentatges competitius de precisió a l’experimentació.La presente tesis doctoral contribuye al estudio de las cláusulas de Horn en lógicas difusas, así como a su uso en representación difusa del conocimiento aplicada al diseño de un algoritmo de clasificación de pinturas según su estilo artístico. En la primera parte del trabajo nos centramos en algunas nociones relevantes para la programación lógica, como lo son por ejemplo los modelos libres y las estructuras de Herbrand en lógica matemática difusa. Así pues, probamos la existencia de modelos libres en clases universales difusas de Horn y demostramos que toda teoría difusa universal de Horn sin igualdad tiene un modelo de Herbrand. Asimismo, introducimos dos nociones de minimalidad para modelos libres, y demostramos que estas nociones son equivalentes en el caso de las fully named structures. En la segunda parte de la tesis doctoral, utilizamos cláusulas de Horn combinadas con el modelado cualitativo como marco de representación difusa del conocimiento para la categorización de estilos de pintura artística. Finalmente, diseñamos un clasificador de pinturas basado en cláusulas de Horn evaluadas, descriptores cualitativos de colores y explicaciones. Este algoritmo, que llamamos l-SHE, proporciona razones de los resultados obtenidos y obtiene porcentajes competitivos de precisión en la experimentación.
This PhD thesis contributes to the systematic study of Horn clauses of predicate fuzzy logics and their use in knowledge representation for the design of an art painting style classification algorithm. We first focus the study on relevant notions in logic programming, such as free models and Herbrand structures in mathematical fuzzy logic. We show the existence of free models in fuzzy universal Horn classes, and we prove that every equality-free consistent universal Horn fuzzy theory has a Herbrand model. Two notions of minimality of free models are introduced, and we show that these notions are equivalent in the case of fully named structures. Then, we use Horn clauses combined with qualitative modeling as a fuzzy knowledge representation framework for art painting style categorization. Finally, we design a style painting classifier based on evaluated Horn clauses, qualitative color descriptors, and explanations. This algorithm, called l-SHE, provides reasons for the obtained results and obtains percentages of accuracy in the experimentation that are competitive.
Universitat Autònoma de Barcelona. Programa de Doctorat en Ciència Cognitiva i Llenguatge
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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.
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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a master step which is in charge of enforcing the constraints, while the training phase is delegated to a learner. In this work, we extend the algorithm in order to deal with semi-supervised learning and soft constraints. In particular, we focus our empirical evaluation on both regression and classification tasks involving monotonicity shape constraints. We demonstrate that our method is robust with respect to its hyperparameters, as well as being able to generalize very well while reducing the number of violations on the enforced constraints. Additionally, the method can even outperform, both in terms of accuracy and constraint satisfaction, other state-of-the-art techniques such as Lattice Models and Semantic-based Regularization with a Lagrangian Dual approach for automatic hyperparameter tuning.
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Nilsson, Linus. "Explainable Artificial Intelligence for Reinforcement Learning Agents." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294162.
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Following the success that machine learning has enjoyed over the last decade, reinforcement learning has become a prime research area for automation and solving complex tasks. Ranging from playing video games at a professional level to robots collaborating in picking goods in warehouses, the applications of reinforcement learning are numerous. The systems are however, very complex and the understanding of why the reinforcement learning agents solve the tasks given to them in certain ways are still largely unknown to the human observer. This makes the actual use of the agents limited to non-critical tasks and the information that could be learnt by them hidden. To this end, explainable artificial intelligence (XAI) has been a topic that has received more attention in the last couple of years, in an attempt to be able to explain the machine learning systems to the human operators. In this thesis we propose to use model-agnostic XAI techniques combined with clustering techniques on simple Atari games, as well as proposing an automated evaluation for how well the explanations explain the behavior of the agents. This in an effort to uncover to what extent model-agnostic XAI can be used to gain insight into the behavior of reinforcement learning agents. The tested methods were RISE, t-SNE and Deletion. The methods were evaluated on several different agents trained on playing the Atari-breakout game and the results show that they can be used to explain the behavior of the agents on a local level (one individual frame of a game sequence), global (behavior over the entire game sequence) as well as uncovering different strategies used by the agents and as training time differs between agents.Efter framgångarna inom maskininlärning de senaste årtiondet har förstärkningsinlärning blivit ett primärt forskningsämne för att lösa komplexa uppgifter och inom automation. Tillämpningarna är många, allt från att spela datorspel på en professionell nivå till robotar som samarbetar för att plocka varor i ett lager. Dock så är systemen väldigt komplexa och förståelsen kring varför en agent väljer att lösa en uppgift på ett specifikt sätt är okända för en mänsklig observatör. Detta gör att de praktiska tillämpningarna av dessa agenter är begränsade till icke-kritiska system och den information som kan användas för att lära ut nya sätt att lösa olika uppgifter är dolda. Utifrån detta så har förklarbar artificiell intelligens (XAI) blivit ett område inom forskning som fått allt mer uppmärksamhet de senaste åren. Detta för att kunna förklara maskininlärningssystem för den mänskliga användaren. I denna examensrapport föreslår vi att använda modelloberoende XAI tekniker kombinerat klustringstekniker på enkla Atarispel, vi föreslår även ett sätt att automatisera hur man kan utvärdera hur väl en förklaring förklarar beteendet hos agenterna. Detta i ett försök att upptäcka till vilken grad modelloberoende XAI tekniker kan användas för att förklara beteenden hos förstärkningsinlärningsagenter. De testade metoderna var RISE, t-SNE och Deletion. Metoderna utvärderades på flera olika agenter, tränade att spelaAtari-breakout. Resultatet visar att de kan användas för att förklara beteendet hos agenterna på en lokal nivå (en individuell bild ur ett spel), globalt beteende (över den totala spelsekvensen) samt även att metoderna kan hitta olika strategier användna av de olika agenterna där mängden träning de fått skiljer sig.
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Karlsson, Marcus. "Developing services based on Artificial Intelligence." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73090.
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This thesis explores the development process of services based on artificial intelligence (AI) technology within an industrial setting. There has been a renewed interest in the technology and leading technology companies as well as many start-ups has integrated it into their market offerings. The technology´s general application potential for enhancing products and services along with the task automation possibility for improved operational excellence makes it a valuable asset for companies. However, the implementation rate of AI services is still low for many industrial actors. The research in the area has been technically dominated with little contribution from other disciplines. Therefore, the purpose of this thesis is to identify development challenges of AI services and drawing on service development- and value-theory to propose a process framework promoting implementation. The work will have two main contributions. Firstly, to compare differences in theoretical and practical development challenges and secondly to combine AI with service development and value theory. The empirical research is done through a single case study based on a systematic combining research approach. It moves iteratively between the theory and empirical findings to direct and support the thesis throughout the work process. The data was collected through semi-structured interviews with a purposive sample. It consisted of two groups of interview participants, one AI expert group and one case internal group. This was supported by participant observation of the case environment. The data analysis was done through flexible pattern matching. The results were divided into two sections, practical challenges and development aspect of AI service development. These were combined with the selected theories and a process framework was generated. The study showed a current understudied area of business and organisational aspect regarding AI service development. Several such challenges were identified with limited theoretical research as support. For a wider industrial adoption of AI technology, more research is needed to understand the integration into the organisation. Further, sustainability and ethical aspect were found not to be a primary concern, only mention in one of the interviews. This, despite the plethora of theory and identified risks found in the literature. Lastly, the interdisciplinary research approach was found to be beneficial to the AI field to integrate the technology into an industrial setting. The developed framework could draw from existing service development models to help manage the identified challenges.Denna uppsats utforskar utvecklingsprocessen av tjänster baserade på artificiell intelligens (AI) i en industriell miljö. Tekniken har fått ett förnyat intresse vilket har lett till att allt fler ledande teknik företag och start-up:s har integrerat AI i deras marknads erbjudande. Teknikens generella applikations möjlighet för att kunna förbättra produkter och tjänster tillsammans med dess automatiserings möjlighet för ökad operationell effektivitet gör den till en värdefull tillgång för företag. Dock så är implementations graden fortfarande låg för majoriteten av industrins aktörer. Forskningen inom AI området har varit mycket teknik dominerat med lite bidrag från andra forskningsdiscipliner. Därför syftar denna uppsats att identifiera utvecklingsutmaningar med AI tjänster och genom att hämta delar från tjänsteutveckling- och värde teori generera ett processramverk som premierar implementation. Uppsatsen har två huvudsakliga forskningsbidrag. Först genom att jämföra skillnader mellan teoretiska och praktiska utvecklingsutmaningar, sedan bidra genom att kombinera AI med tjänsteutveckling- och värdeteori. Den empiriska forskningen utfördes genom en fallstudie baserad på ett systematic combining tillvägagångsätt. På så sätt rör sig forskning iterativt mellan teori och empiri för att forma och stödja uppsatsen genom arbetet. Datat var insamlad genom semi strukturerade intervjuer med två separata, medvetet valda intervjugrupper där ena utgjorde en AI expert grupp och andra en intern grupp för fallstudien. Detta stöttades av deltagande observationer inom fallstudiens miljö. Dataanalysen utfördes med metoden flexible pattern matching. Resultatet var uppdelat i två olika sektioner, den första med praktiska utmaningar och den andra med utvecklingsaspekter av AI tjänsteutveckling. Dessa kombinerades med de utvalda teorierna för att skapa ett processramverk. Uppsatsen visar ett under studerat område angående affär och organisation i relation till AI tjänsteutveckling. Ett flertal av sådana utmaningar identifierades med begränsat stöd i existerande forskningslitteratur. För en mer utbredd adoption av AI tekniken behövs mer forskning för att förstå hur AI ska integreras med organisationer. Vidare, hållbarhet och etiska aspekter var inte en primär aspekt i resultatet, endast bemött i en av intervjuerna trots samlingen av artiklar och identifierade risker i litteraturen. Till sist, det tvärvetenskapliga angreppsättet var givande för AI området för att bättre integrera tekniken till en industriell miljö. Det utvecklade processramverket kunde bygga på existerande tjänsteutvecklings modeller för att hantera de identifierade utmaningarna.
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Rouget, Thierry. "Learning explainable concepts in the presence of a qualitative model." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/9762.
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This thesis addresses the problem of learning concept descriptions that are interpretable, or explainable. Explainability is understood as the ability to justify the learned concept in terms of the existing background knowledge. The starting point for the work was an existing system that would induce only fully explainable rules. The system performed well when the model used during induction was complete and correct. In practice, however, models are likely to be imperfect, i.e. incomplete and incorrect. We report here a new approach that achieves explainability with imperfect models. The basis of the system is the standard inductive search driven by an accuracy-oriented heuristic, biased towards rule explainability. The bias is abandoned when there is heuristic evidence that a significant loss of accuracy results from constraining the search to explainable rules only. The users can express their relative preference for accuracy vs. explainability. Experiments with the system indicate that, even with a partially incomplete and/or incorrect model, insisting on explainability results in only a small loss of accuracy. We also show how the new approach described can repair a faulty model using evidence derived from data during induction.
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Amarasinghe, Kasun. "Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6091.
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Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential.
The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical Systems (CP-ADS). The main objective was broken down into three sub-objectives: 1) Identifying key-requirements that an explainable CP-ADS should satisfy, 2) Developing supervised ANN-based explainable CP-ADSs, 3) Developing unsupervised ANN-based explainable CP-ADSs.
In achieving those objectives, this dissertation provides the following contributions: 1) a set of key-requirements that an explainable CP-ADS should satisfy, 2) a methodology for deriving summaries of the knowledge of a trained supervised CP-ADS, 3) a methodology for validating derived summaries, 4) an unsupervised neural network methodology for learning cyber-physical (CP) behavior, 5) a methodology for visually and linguistically explaining the learned CP behavior.
All the methods were implemented on real-world and benchmark datasets. The set of key-requirements presented in the first contribution was used to evaluate the performance of the presented methods. The successes and limitations of the presented methods were identified. Furthermore, steps that can be taken to overcome the limitations were proposed. Therefore, this dissertation takes several necessary steps toward developing explainable ANN-based CP-ADS and serves as a framework that can be expanded to develop trustworthy ANN-based CP-ADSs.
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Kim, Jee Won. "How speciesism affects artificial intelligence (AI) adoption intent." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/228673/1/Jee%20Won_Kim_Thesis.pdf.
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As there have been concerns about the excessive advancement of artificial intelligence (AI) surpassing humans, exploring reactions to AI as challenging human superiority is meaningful. By examining how the hierarchical and discriminative views on animals (speciesism) affects the views on non-living AI, this thesis has significant and novel contributions to AI adoption literature and AI product marketing.
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Fatima, Samar. "Mapping artificial intelligence affordances for the public sector." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235926/1/Samar%2BFatime%2BThesis.pdf.
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This thesis explores the affordances of artificial intelligence (AI) for the public sector. The thesis consists of three studies that answer what, why and how questions of AI affordance actualization in public sector using a combination of primary and secondary data sources. In this thesis, the affordance theory lens is used to explore AI affordance perception and actualization for the public sector through three related studies. The perception of AI affordance is investigated in the first two studies. The third study designed and evaluated artefact for public agencies to actualize AI affordance.
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Malmborn, Albin, and Linus Sjöberg. "Implementing Artificial intelligence." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20942.
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Den här uppsatsen har som syfte att undersöka huruvida det är möjligt att ta fram riktlinjer för vad privata verksamheter behöver ta i beaktande inför en planerad implementering av artificiell intelligens. Studien kommer belysa faktorer som hjälper företag att förstå vad som krävs inför en sådan omställning, men även de hinder som måste övervinnas för att lyckas. Studiens datainsamling har genomförts med två metoder, först en litteraturstudie sedan kvalitativa, semistrukturerade forskningsintervjuer. Dessa har sedan analyserats med vars en analysmetod som kompletterar varandra och därefter tolkats för att se mönster som kan besvara studiens frågeställning: Vad måste svenska organisationer inom den privata sektorn beakta för att lyckas implementera Artificiell intelligens i sin verksamhet? Resultatet har tagits fram genom att jämföra vetenskapliga texter och intervjuer, för att undersöka om den akademiska och praktiska synen skiljer sig åt. Studien resulterade i åtta faktorer som företag borde ta i beaktning inför en implementering av artificiell intelligens. Författarna hoppas att med den här studien kunna främja svensk utveckling inom artificiell intelligens och på så vis generera ett större nationellt mervärde och en starkare konkurrenskraft internationell.The purpose of this paper is to investigate the possibilities to develop guidelines for businesses to take into account before an implementation of artificial intelligence. The study will highlight different factors that will help companies to understand what is required to make this kind of digital transition, it will also highlight the obstacles companies have to overcome in order to succeed. The data collection was conducted in two parts, first a literature study and then qualitative, semi-structured interviews. These were analyzed with their own analysis which supplement each other, and interpreted to identify patterns that could answer the study's main question: What must Swedish organizations in the private sector consider in order to successfully implement Artificial Intelligence in their operations?The result of the study has been produced by comparing scientific texts and interviews, to investigate whether the academic and practical views differ. The study resulted in eight factors that companies should consider before implementing artificial intelligence. The authors hope that the study will promote Swedish development in artificial intelligence and thus generate a greater national value and international competitiveness.
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Edlund, Mattias. "Artificial Intelligence in Games : Faking Human Behavior." Thesis, Uppsala universitet, Institutionen för speldesign, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-258222.
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This paper examines the possibilities of faking human behavior with artificial intelligence in computer games, by using efficient methods that save valuable development time and also creates a more rich experience for the players of a game. The specific implementation of artificial intelligence created and discussed is a neural network controlling a finite-state machine. The objective was to mimic human behavior rather than simulating true intelligence. A 2D shooter game is developed and used for experiments performed with human and artificial intelligence controlled players. The game sessions played were recorded in order for other humans to replay. Both players and spectators of the game sessions left feedbacks and reports that could later be analyzed. The data collected from these experiments was then analyzed, and reflections were made on the entire project. Tips and ideas are proposed to developers of shooter games who are interested in making human-like artificial intelligence. Conclusions are made and extra information is provided in order to further iterate on this research.Denna rapport undersöker möjligheterna att förfalska mänskligt beteende genom artificiell intelligens i datorspel, med hjälp av effektiva metoder som sparar värdefull utvecklingstid och som även skapar en rikare upplevelse för spelare. Den specifika implementationen av artificiell intelligens som utvecklas och diskuteras är ett neuralt nätverk som kontrollerar en finite-state machine. Målet var att efterlikna mänskligt beteende snarare än att simulera verklig intelligens. Ett 2D shooter-spel utvecklas och används för utförda experiment med mänskliga och artificiell intelligens-kontrollerade spelare. De sessioner som spelades under experimenten spelades in, för att sedan låta andra människor titta på inspelningarna. Både spelare och åskådare av spelsessionerna lämnade återkoppling och rapporter för senare analysering. Datan som samlats in från experimenten analyserades, och reflektioner utfördes på hela projektet. Tips och idéer presenteras till utvecklare av shooter-spel som är intresserade av en mer människolik artificiell intelligens. Slutsatser läggs fram och extra information presenteras för att kunna fortsätta iterera vidare på denna undersökning.
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Khan, Laiq. "Hybrid AI paradigms applied to power system damping controls." Thesis, University of Strathclyde, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273412.
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Keneni, Blen M. Keneni. "Evolving Rule Based Explainable Artificial Intelligence for Decision Support System of Unmanned Aerial Vehicles." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525094091882295.
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Pierrard, Régis. "Explainable Classification and Annotation through Relation Learning and Reasoning." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST008.
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Avec les succés récents de l’apprentissage profond et les interactions toujours plus nombreuses entre êtres humains et intelligences artificielles, l’explicabilité est devenue une préoccupation majeure. En effet, il est difficile de comprendre le comportement des réseaux de neurones profonds, ce qui les rend inadaptés à une utilisation dans les systèmes critiques. Dans cette thèse, nous proposons une approche visant à classifier ou annoter des signaux tout en expliquant les résultats obtenus. Elle est basée sur l’utilisation d’un modèle transparent, dont le raisonnement est clair, et de relations floues interprétables qui permettent de représenter l’imprécision du langage naturel.Au lieu d’apprendre sur des exemples sur lesquels les relations ont été annotées, nous proposons de définir un ensemble de relations au préalable. L’évaluation de ces relations sur les exemples de la base d’entrainement est accélérée grâce à deux heuristiques que nous présentons. Ensuite, les relations les plus pertinentes sont extraites en utilisant un nouvel algorithme de frequent itemset mining flou. Ces relations permettent de construire des règles pour la classification ou des contraintes pour l’annotation. Ainsi, une explication en langage naturel peut être générée.Nous présentons des expériences sur des images et des séries temporelles afin de montrer la généricité de notre approche. En particulier, son application à l’annotation d’organe explicable a été bien évaluée par un ensemble de participants qui ont jugé les explications convaincantes et cohérentesWith the recent successes of deep learning and the growing interactions between humans and AIs, explainability issues have risen. Indeed, it is difficult to understand the behaviour of deep neural networks and thus such opaque models are not suited for high-stake applications. In this thesis, we propose an approach for performing classification or annotation and providing explanations. It is based on a transparent model, whose reasoning is clear, and on interpretable fuzzy relations that enable to express the vagueness of natural language.Instead of learning on training instances that are annotated with relations, we propose to rely on a set of relations that was set beforehand. We present two heuristics that make the process of evaluating relations faster. Then, the most relevant relations can be extracted using a new fuzzy frequent itemset mining algorithm. These relations enable to build rules, for classification, and constraints, for annotation. Since the strengths of our approach are the transparency of the model and the interpretability of the relations, an explanation in natural language can be generated.We present experiments on images and time series that show the genericity of the approach. In particular, the application to explainable organ annotation was received positively by a set of participants that judges the explanations consistent and convincing
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Svedberg, Peter O. S. "Steps towards an empirically responsible AI : a methodological and theoretical framework." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-246.
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Initially we pursue a minimal model of a cognitive system. This in turn form the basis for the development of amethodological and theoretical framework. Two methodological requirements of the model are that explanation be from the perspective of the phenomena, and that we have structural determination. The minimal model is derived from the explanatory side of a biologically based cognitive science. Fransisco Varela is our principal source for this part. The model defines the relationship between a formally defined autonomous system and an environment, in such a way as to generate the world of the system, its actual environment. The minimal model is a modular explanation in that we find it on different levels in bio-cognitive systems, from the cell to small social groups. For the latter and for the role played by artefactual systems we bring in Edwin Hutchins' observational study of a cognitive system in action. This necessitates the introduction of a complementary form of explanation. A key aspect of Hutchins' findings is the social domain as environment for humans. Aspects of human cognitive abilities usually attributed to the person are more properly attributed to the social system, including artefactual systems.
Developing the methodological and theoretical framework means making a transition from the bio-cognitive to the computational. The two complementary forms of explanation are important for the ability to develop a methodology that supports the construction of actual systems. This has to be able to handle the transition from external determination of a system in design to internal determination (autonomy) in operation.
Once developed, the combined framework is evaluated in an application area. This is done by comparing the standard conception of the Semantic Web with how this notion looks from the perspective of the framework. This includes the development of the methodological framework as a metalevel external knowledge representation. A key difference between the two approaches is the directness by which the semantic is approached. Our perspective puts the focus on interaction and the structural regularities this engenders in the external representation. Regularities which in turn form the basis for machine processing. In this regard we see the relationship between representation and inference as analogous to the relationship between environment and system. Accordingly we have the social domain as environment for artefactual agents. For human level cognitive abilities the social domain as environment is important. We argue that a reasonable shortcut to systems we can relate to, about that very domain, is for artefactual agents to have an external representation of the social domain as environment.
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Rafiq, M. Y. "Artificial intelligence techniques for the structural design of buildings." Thesis, University of Strathclyde, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382446.
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Casten, Carlberg Carl Johan, and Elsa Jerhamre. "Artificial Intelligence in Agriculture : Opportunities and Challenges." Thesis, Uppsala universitet, Avdelningen för datorteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443576.
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Artificial Intelligence (AI) is increasingly used in different parts of society for providing decision support in various activities. The agricultural sector is anticipated to benefit from an increased usage of AI and smart devices, a concept called smart farming technologies. Since the agricultural sector faces several simultaneous challenges, such as shrinking marginals, complicated pan-European regulations, and demands to mitigate the environmental footprint, there are great expectations that smart farming will benefit both individual farmers and industry stakeholders. However, most previous research focuses only on a small set of characteristics for implementing and optimising specific smart farming technologies, without considering all possible aspects and effects. This thesis investigates both technical and non-technical opportunities and hurdles when implementing AI in Swedish agricultural businesses. Three sectors in agriculture are scrutinized: arable farming, milk production and beef production. As a foundation for the thesis, a literature review revises former research on smart farming. Thereafter, an interview study with 27 respondents both explores the susceptibility and maturity of smart farming technologies and provides examples of technical requirements of three chosen applications of AI in agriculture. Findings of the study include a diverse set of aspects that both enable and obstruct the transition. Main identified opportunities are the importance smart farming has on the strategic agendas of several industry stakeholders, the general trend towards software technology as a service through shared machinery, the vast amount of existing data, and the large interest from farmers towards new technology. Contrasting, the thesis identifies main hurdles as technical and legislative challenges to data ownership, potential cybersecurity threats, the need for a well-articulated business case, and the sometimes lacking technical knowledge within the sector. The thesis concludes that the macro trend points towards a smart farming transition but that the speed of the transformation will depend on the resolutions for the identified obstacles.
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Houzé, Étienne. "Explainable Artificial Intelligence for the Smart Home : Enabling Relevant Dialogue between Users and Autonomous Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252705.
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Smart home technologies aim at providing users control over their house, including but not limited to temperature, light, security, energy consumption. Despite offering the possibility of lowering power consumption (and thus the energy bill), smart home systems still struggle to convince a broad public, often being regarded as intrusive or not trustworthy. Gaining sympathy from users would require a solution to provide relevant explanations without relying on distant computing (which would imply sending private data online). We therefore propose an architecture where the autonomic controller system is extended with an intelligent layer aiming at translating autonomic values into concepts and finding causality relations in order to explain the logic behind the decisions and the state of the system to the user.Smart-home-teknik syftar till att ge användarna bättre kontroll över sina hem till exempel med avseende på temperatur, ljus, säkerhet eller förbrukning av energi. Denna teknik har svårt att övertyga många kunder trots löften om energibesparing och komfort. Tekniken ses som påträngande. En lösning som ökar användarnas välvilja genom att ge relevanta förklaringar men utan att skicka privata data online är önskvärd. I detta examensarbete föreslår vi en arkitektur där ett intelligent lager läggs ovanpå det autonoma systemet. Det intelligenta lagret försöker dels översätta autonoma termer till begrepp som användaren förstår och dels att hitta kausala relationer som förklarar systemets tillstånd och beslut som fattas av systemet.
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Othman, Amani. "Mapping Artificial Intelligence (AI) Capabilities around Human Competences : An explorative study." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44748.
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Technology in the workplace has helped increase the rate of production and speed at which business occur. As these technologies advance, humans are becoming more efficient and more productive than ever before. The impact of technology on work, has consistently changed the way human across every industry do their jobs. In fact, the latest advancements in artificial intelligence (AI) has the potential to further influence a greater shift in the way human accomplish job tasks. Because artificial intelligence is a technology based on augmenting human, we can use this advantage to further complement human intelligence. Making human more creative and smarter than they have ever been. To achieve this, we need to understand how AI’s capabilities can complement human’s competences. In a workplace context, we need to understand how AI can facilitate the support of different type of tasks. Hence, the purpose of this explorative study is to understand AI’s influence on people’s skills and on workflows. This is done by mapping AI capabilities around human competences to help individuals up- skill and to support organizations in planning effective workflows enabled by AI to achieve better performance. The insights for this qualitative study is gathered by developing an AI use- case with industrial partners, while carefully observing the influence AI poses on human skills and organization processes. Beyond the traditional technology optimizations that earlier helped human with speed and accuracy, the findings from this thesis show that AI could empower human through skills such as creativity, problem solving, and analysis. As a consequence, such advancement impacts the demand of human competences needed to thrive in AI enabled workplaces. The long-term impact of adapting AI to support in workplace tasks, shows a trend in moving toward more intellectually demanding tasks. While workflows will need to facilitate collaboration between cross-disciplinary teams and support high involvement.
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Savola, Hannimari, and Bijona Troqe. "Recruiters just wanna have...AI? : Implications of implementing AI in HR recruitment." Thesis, Linköpings universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158480.
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The development and implementation of AI is rapidly growing in the Nordic countries, yet the perception and information of AI is still limited. This paper will look deeper into the managerial aspects of implementing AI as part of the recruitment system, specifically the selection process and machine learning in text mining. The data gathering of this research has been conducted via interviews with Linköping's municipality, as well as collecting secondary data from public reports and scientific articles. Afterwards, the data was then scrutinized through theoretical analysis, using frameworks from different academic researches. A set of aspects was found, which affects the implementation of AI in an organisation in Sweden. A managerial view was taken to find a deeper significance on why an understanding of these aspects is necessary when implementing AI as a part of company's recruitment processes. However, while other elements other than the ones identified in this thesis do exist, a coherent picture of the process and the affecting variables can be thoroughly explained through these specifically chosen viewpoints. The paper concludes with drawing a bigger image of the AI in recruitment and selection processes, and the implications of it to an organisation considering to implement AI as part of these processes in near future. The thesis can be seen as a recommendation to any establishment that is making the decision of adopting the usage of AI as part of recruitment.
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Shamsolmaali, A. "Artificial intelligence and physiological models in medicine : A prototypical approach." Thesis, City University London, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382837.
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O'Rell, James L. "Smart Terrain using Multiple Needs." Youngstown State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1338040302.
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Srinivasan, Venkatesh. "On designing an AI based generic scheduling framework." Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1061472619.
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Hedström, Anna. "Explainable Artificial Intelligence : How to Evaluate Explanations of Deep Neural Network Predictions using the Continuity Test." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281279.
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With a surging appetite to leverage deep learning models as means to enhance decisionmaking, new requirements for interpretability are set. Renewed research interest has thus been found within the machine learning community to develop explainability methods that can estimate the influence of a given input feature to the prediction made by a model. Current explainability methods of deep neural networks have nonetheless shown to be far from fault-proof and the question of how to properly evaluate explanations has largely remained unsolved. In this thesis work, we have taken a deep look into how popular, state-of-the-art explainability methods are evaluated with a specific focus on one evaluation technique that has proved particularly challenging - the continuity test. The test captures the sensitivity of explanations by measuring how an explanation, in relation to a model prediction, changes when its input is perturbed. While it might sound like a reasonable expectation that the methods behave consistently in their input domain, we show in experiments on both toy-and real-world data, on image classification tasks, that there is little to no empirical association between how explanations and networks respond to perturbed inputs. As such, we challenge the proposition that explanations and model outcomes can, and should be, compared. As a second line of work, we also point out how and why it is problematic that commonly applied ad-hoc perturbation techniques tend to produce samples that lie far from the data distribution. In the pursuit for better, more plausible image perturbations for the continuity test, we therefore present an alternative approach that relies on sampling in latent space, as learned by a probabilistic, generative model. To this end, we hope that the work presented in this thesis will not only be helpful in identifying limitations of current evaluation techniques, but that the work also contributes with ideas of how to improve them.Med ett ökat intresse att använda djupa neurala nätverk i olika delar av samhället, ställs nya krav på dess tolkbarhet. Förnyat forskningsintresse har således infunnit sig för att utveckla förklaringsmodeller som kan redogöra varför ett beslut har tagits av en algoritm. Förklaringsmodellerna har dock visat sig vara långt ifrån felfria och frågan om hur man utvärderar dem har i stort sett förblivit obesvarad. I detta arbete har vi undersökt hur populära förklaringsmodeller evalueras med ett specifikt fokus på en evalueringsteknik som har visat sig vara särskilt problematisk - “kontinuitetstestet”. Testet fångar upp förklaringarnas “sensitivitet” genom att mäta hur en förklaring, i förhållande till modellens prediktion, förändras när dess input modifieras. Även om det kan låta som en rimlig förväntning att förklaringsmetoderna skall uppträda konsekvent i sin inputdomän, så visar vi i bildklassificeringsexperiment på både syntetisk och real data, att det empiriska sambandet mellan hur förklaringar och nätverk svarar på modifierad input är obetydlig. Därmed utmanar vi idén om att förklaringar och modellresultat kan och bör jämföras. Som en andra inriktning i detta arbete påpekar vi också hur och varför det är problematiskt att vanligt förekommande bildmanipuleringstekniker tenderar att producera bilder som ligger långt ifrån den ursprungliga datadistributionen. I strävan efter mer trovärdiga och bättre bildmanipuleringstekniker för kontinuitetstestet, presenterar vi därför ett alternativt tillvägagångssätt som förlitar sig på sampling i det latenta rummet, producerat av en probabilistisk generativ modell. För detta ändamål hoppas vi därför att arbetet som presenteras i denna examensarbete inte bara belyser brister i aktuella utvärderingstekniker, utan också bidrar med idéer om hur man kan förbättra dem.
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Bahers, Quentin. "AI Planning-Based Service Modeling for the Internet of Things." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-246212.
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It is estimated that by 2020, more than 50 billion devices will be interconnected, to form what is called the Internet of Things. Those devices range from consumer electronics to utility meters, including vehicles. Provided with sensory capabilities, those objects will be able to transmit valuable information about their environment, not only to humans, but even more importantly to other machines, which should ultimately be able to interpret and take decisions based on the information received. This “smartness” implies gifting those devices with a certain degree of automation. This Master’s Thesis investigates how recent advances in artificial intelligence planning can be helpful in building such systems. In particular, an artificial intelligence planner able to generate workflows for most of IoT-related use cases has been connected to an IoT platform. A performance study of a state-of-the planner, Fast Downward, on one of the most challenging IoT application, Smart Garbage Collection (which is similar to the Traveling Salesman Problem) has also been carried out. Eventually, different pre-processing and clustering techniques are suggested to tackle the latest AI planners’ inefficiency on quickly finding plans for the most difficult tasks.
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Fyrvald, Johanna. "Mitigating algorithmic bias in Artificial Intelligence systems." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388627.
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Artificial Intelligence (AI) systems are increasingly used in society to make decisions that can have direct implications on human lives; credit risk assessments, employment decisions and criminal suspects predictions. As public attention has been drawn towards examples of discriminating and biased AI systems, concerns have been raised about the fairness of these systems. Face recognition systems, in particular, are often trained on non-diverse data sets where certain groups often are underrepresented in the data. The focus of this thesis is to provide insights regarding different aspects that are important to consider in order to mitigate algorithmic bias as well as to investigate the practical implications of bias in AI systems. To fulfil this objective, qualitative interviews with academics and practitioners with different roles in the field of AI and a quantitative online survey is conducted. A practical scenario covering face recognition and gender bias is also applied in order to understand how people reason about this issue in a practical context. The main conclusion of the study is that despite high levels of awareness and understanding about challenges and technical solutions, the academics and practitioners showed little or no awareness of legal aspects regarding bias in AI systems. The implication of this finding is that AI can be seen as a disruptive technology, where organizations tend to develop their own mitigation tools and frameworks as well as use their own moral judgement and understanding of the area instead of turning to legal authorities.
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Panesar, Kulvinder. "Natural language processing (NLP) in Artificial Intelligence (AI): a functional linguistic perspective." Vernon Press, 2020. http://hdl.handle.net/10454/18140.
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YesThis chapter encapsulates the multi-disciplinary nature that facilitates NLP in AI and reports on a linguistically orientated conversational software agent (CSA) (Panesar 2017) framework sensitive to natural language processing (NLP), language in the agent environment. We present a novel computational approach of using the functional linguistic theory of Role and Reference Grammar (RRG) as the linguistic engine. Viewing language as action, utterances change the state of the world, and hence speakers and hearer’s mental state change as a result of these utterances. The plan-based method of discourse management (DM) using the BDI model architecture is deployed, to support a greater complexity of conversation. This CSA investigates the integration, intersection and interface of the language, knowledge, speech act constructions (SAC) as a grammatical object, and the sub-model of BDI and DM for NLP. We present an investigation into the intersection and interface between our linguistic and knowledge (belief base) models for both dialogue management and planning. The architecture has three-phase models: (1) a linguistic model based on RRG; (2) Agent Cognitive Model (ACM) with (a) knowledge representation model employing conceptual graphs (CGs) serialised to Resource Description Framework (RDF); (b) a planning model underpinned by BDI concepts and intentionality and rational interaction; and (3) a dialogue model employing common ground. Use of RRG as a linguistic engine for the CSA was successful. We identify the complexity of the semantic gap of internal representations with details of a conceptual bridging solution.
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Arnold, Nathan S. "Reexamining Deus ex Machina: Artificial Intelligence, Theater, & a New Work." Ohio University Honors Tutorial College / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1569001838298508.
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Güler, Dilvan, and Mohamed Mahdi. "A Systematic Mapping Study on APIs Utilizing Artificial Intelligence Technology." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300093.
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This thesis covers the systematic mapping of established public Application Programming Interface (API)s that are employing the Artificial Intelligence (AI) technology. This due to the fact that the problem has been the lack of systematic maps of AI APIs in the present time, therefore this thesis has the purpose of increasing the insight in the area by creating the mapping study. The goal is to provide both a basis for research and to aid the general developer which uses the AI APIs. The systematic mapping of the AI APIs will be conducted by examining the information of the APIs and iterations classifying the AI APIs into categories and presented in tables. The analysis and discussion of the study was made based on the results from the study, namely the phases, the iterations, the result tables and the final systematic map. Additionally, an analysis was made on the validity threats of the study. The evaluation of each API in this study was done in cycles, by categorizing each AI API into a category that is included in the final result, which is a systematic map. The result has been proven to be useful for the target group of this study, the researchers and developers, by aiding them in finding the right API for them to use in their work. Therefore, this work will help future developers and researchers due to the fact that the thesis is providing relevant information in the development phases of employing AI in the web interfaces at the present time.Detta examensarbete omfattar en systematisk kartläggning av etablerade publika API:er som använder sig utav AI- teknik. Eftersom bristen på systematiska kartor över AI API:er varit stor, har detta examensarbete syfte att öka insikten i området genom att skapa denna kartläggningsstudie. Målet med den systematiska kartläggningen är att bygga en grund för framtida forskning inom detta område, samt förenkla processen av att hitta AI API:erna för den allmänna utvecklaren som använder dessa. Den systematiska kartläggningen av AI API:er kommer att genomföras genom att undersöka tillgänglig dokumentation och information om API:erna. Dessutom klassificerades AI API:erna i kategorier, och presenterades sedan i tabeller. Analysen och diskussionen av studien gjordes baserat på resultaten från studien, nämligen faserna och iterationerna där AI API:erna kategoriseras, samt på resultat tabellerna och den slutliga systematiska kartan på AI API:erna. Dessutom gjordes en analys av hoten mot studiens giltighet. Utvärderingen av varje API i denna studie gjordes i cykler genom att kategorisera varje AI API i en kategori som sedan inkluderades i resultatet vilket är en systematiska karta. Resultatet från detta examensarbete har visat sig vara användbar för målgruppen, forskare och utvecklare, då det hjälper målgruppen att hitta rätt API att använda i arbetet.
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Phillips, Eve M. (Eve Marie) 1977. "If it works, it's not AI : a commercial look at artificial intelligence startups." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80558.
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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 76-80).
by Eve M. Phillips.
S.B.and M.Eng.
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Iqbal, Tahir, and Md Nazmul Khan. "The Impact of Artificial Intelligence (AI) on CRM and Role of Marketing Managers." Thesis, Högskolan i Gävle, Avdelningen för ekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-35225.
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Background- Emergence of artificial intelligence (AI) has transformed the dynamics of the business world. AI tools are changing the way marketers used to do business and these changes are so profound that it has become difficult for marketing managers to overlook the importance of investing in and adopting AI as an integral part of the marketing function. Companies which have implemented AI in their Customer Relationship Management (CRM) and marketing as a whole,have reported significant impacts on company’s growth in terms of customer loyalty and profitability. In recent years, we also witness emergence of specialized software and IT companies which are dedicated to produce customized marketing solutions and programs targeted at customer relationship management and marketing. They produce enterprise applications for marketing automation, analytics, and application development. Consequently, the role of marketing managers has also transformed and is expected to change even more in the future. This calls for further research, as the area appears to have received little attention in relation to its weight of importance. Aim- The main purpose of our thesis is to contribute to the literature of what changes have been introduced in the function of CRM as a result of AI integration and how these ongoing changes have affected the role of marketing managers. Results- The findings of the research show a connection that exists between the 3 factors, Artificial intelligence, CRM and role of marketing managers/decision makers. The ongoing changes that are occurring in marketing as a result of AI are not only limited to transforming the marketing function of business, it is rather consequently changing the way marketing manager make decisions and the way they interpret data. It is becoming increasingly essential for marketing managers to upgrade their skillsets and acquire sound technical knowledge in addition to deep understanding of marketing concepts. Contribution- The evolution of AI is impacting all areas of business and to harvest maximum benefits, adoption and change in all aspects is equally important. This study offers guideline to marketing managers for successful application of AI and its impact on the overall performance.
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Mehta, Manish. "Construction and adaptation of AI behaviors in computer games." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42724.
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Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) in computer games tends to come from people with computing skills well beyond the average user. The
prime reason behind the lack of involvement of novice users in creating AI behaviors for NPC's in computer games is that construction of high quality AI behaviors is a hard problem. There are two reasons for it. First, creating a set of AI behavior requires specialized skills in design and programming. The nature of the process
restricts it to certain individuals who have a certain expertise in this area. There
is little understanding of how the behavior authoring process can be simplified with
easy-to-use authoring environments so that novice users (without programming and
design experience) can carry out the behavior authoring task. Second, the constructed
AI behaviors have problems and bugs in them which cause a break in player expe-
rience when the problematic behaviors repeatedly fail. It is harder for novice users
to identify, modify and correct problems with the authored behavior sets as they do
not have the necessary debugging and design experience. The two issues give rise to
a couple of interesting questions that need to be investigated: a) How can the AI
behavior construction process be simplified so that a novice user (without program-
ming and design experience) can easily conduct the authoring activity and b) How
can the novice users be supported to help them identify and correct problems with
the authored behavior sets?
In this thesis, I explore the issues related to the problems highlighted and propose
a solution to them within an application domain, named Second Mind(SM). In SM
novice users who do not have expertise in computer programming employ an authoring
interface to design behaviors for intelligent virtual characters performing a service
in a virtual world. These services range from shopkeepers to museum hosts. The
constructed behaviors are further repaired using an AI based approach. To evaluate
the construction and repair approach, we conduct experiments with human subjects.
Based on developing and evaluating the solution, I claim that a design solution
with behavior timeline based interaction design approach for behavior construction
supported by an understandable vocabulary and reduced feature representation for-
malism enables novice users to author AI behaviors in an easy and understandable
manner for NPCs performing a service in a virtual world. I further claim that an
introspective reasoning approach based on comparison of successful and unsuccessful
execution traces can be used as a means to successfully identify breaks in player ex-
perience and modify the failures to improve the experience of the player interacting
with NPCs performing a service in a virtual world. The work contributes in the
following three ways by providing: 1) a novel introspective reasoning approach for
successfully detecting and repairing failures in AI behaviors for NPCs performing a
service in a virtual world.; 2) a novice user understandable authoring environment to
help them create AI behaviors for NPCs performing a service in a virtual world in an
easy and understandable manner; and 3) Design, debugging and testing scaffolding
to help novice users modify their authored AI behaviors and achieve higher quality
modified AI behaviors compared to their original unmodified behaviors.
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Lindstam, Tim, and 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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Jönsson, Josef. "AI acceptance and attitudes : People’s perception of healthcare and commercial AI applications." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176507.
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The relevance of AI is ever increasing. The goal of the wide implementation is usually either to boost task efficiency or for public comfort. To fuel this progression, more personal data is being used and Artificial intelligence inhabits the role of the human expert, in many different applications. This study investigated the attitudes and rates of acceptance to said AI applications and if they differed in relation to each other. Additionally, this study explored if general positive and negative attitude towards AI influence AI acceptance. Applications studied came from two different domains, E-commerce/Marketing and Healthcare. It was found that acceptance levels did in fact not significantly differ between the two domains. However, a significant positive correlation was found between positive attitude and acceptance rates, while an inverse significant correlation was found between negative attitude and acceptance rates.
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Guerra, Ana. "Millennial Consumption Values in Artificial Intelligence : An exploratory study of millennial consumer values in artificial intelligence." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-39611.
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Artificial intelligence is rapidly progressing and could be the next technological revolution we see. The idea of AI is no longer farfetched and is becoming more present; individuals are showing a very diverse set of opinions regarding AI. We are currently being the first generation of people to be introduced to AI assets. As this striving new topic is developing the research existing today regarding AI is predominantly based on a technical perspective, and a gap concerning consumer values and AI, applied on millennial’s consumer values is present. The purpose of this study is to explore Millennial consumption values regarding AI with the use of The Theory of Consumption Values as a base theory. When concluded the study will add value to the field and will benefit from future research. The purpose of this study is conducted from a consumer perspective. The study is of qualitative method and the primary, empirical data is gathered through 19 semi structured interviews with millennial. An abductive approach is taken. The finding s of this study show results of the exploration of millennial consumptions values on AI. Furthermore, the study also showed the most important consumption value regarding AI and the one most talked about. Lastly, additional values were found as well as extensions to existing consumption values.
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Lundin, Lowe. "Artificial Intelligence for Data Center Power Consumption Optimisation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447627.
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The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3" could, within the simulated environment, consistently and convincingly outperform the control policy currently in use at Ericsson’s Kista data center.
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Verenich, Ilya. "Explainable predictive monitoring of temporal measures of business processes." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/124037/1/Ilya_Verenich_Thesis.pdf.
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This thesis explores data-driven, predictive approaches to monitor business process performance. These approaches allow process stakeholders to prevent or mitigate potential performance issues or compliance violations in real time, as early as possible. To help users understand the rationale for the predictions and build trust in them, the thesis proposes two techniques for explainable predictive process monitoring: one based on deep learning, the other driven by process models. This is achieved by decomposing a prediction into its elementary components. The techniques are compared against state-of-the-art baselines and a trade-off between accuracy and explainability of the predictions is evaluated.
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Jonson, Maja, and Prajwal Modani. "Diagnosing Doctors and AI : What the introduction of AI can tell us about the professional role of physicians." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448747.
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Artificial Intelligence (AI) is being implemented in various industries and shows promise within healthcare. AI has potential to perform tasks that a professional would traditionally do, and it thus looks to be a disruptive technology. Professional workers, like physicians, are likely to resist such implementations to protect the high status of their profession. As professional workers and healthcare hold significant roles in society, we found that research on how they are affected by AI is needed. This study takes place in the beginning of the introduction in Swedish hospitals. To gain insight into how physicians accept and relate AI to their professional work, we ask the question ‘Do physicians see AI as a threat to their professional role?’ With an abductive, qualitative case-study approach we have conducted eight semi- structured interviews with physicians with various experience and knowledge of working with AI. Theory of institutional work for maintenance allowed for more grounding in answering our question. Further, realisations from previous studies on change served to explain why our findings did not turn out as expected. We highlight a perspective of viewing technological change as a link in a chain of events, rather than studying one implementation at a time.
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Franco, Giulio. "Effects of AI on driving experience." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8457.
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Realism is a very sought feature in interactive driving simulators for traffic studies, since a nonrealistic simulation could produce non-realistic human drivers behaviours. Since cars driven by artificial intelligence (AI) are one of the main components of a traffic simulation, they play an important role in making up the overall sense of realism. A good understanding of how the AI influences human drivers is thus important in avoiding biases in traffic studies with simulators, and might also come useful in simulators for traffic education, in order to induce certain behaviours in the students. The purpose of this study was to build a driving simulation with multiple AI-driven cars, and let human testers use it, in order to analyse if and how a more polite lane-change behaviour, a more realistic lateral alignment, and a slower average speed affect the perception and the behaviour of human drivers. The simulator was developed upon low-cost hardware infrastructure previously used for other traffic studies. Since the existing software is very specific and hard to modify, a new simulation software was built from scratch for this study, using the Unity3D engine and implementing design patterns developed in previous studies, in order to produce a more flexible and modifiable infrastructure than what had been done in the past studies. The test subjects gave a generally good feedback on the simulator as a whole, and cars which politely changed lanes were regarded as behaving in a slightly more realistic way. Some insights were also obtained about user perception, mainly consisting in a difficulty in perceiving absolute speeds, whereas relative speeds were estimated more accurately.
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Fridensköld, Jonatan. "Biased AI : The hidden problem that needs an answer." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17943.
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Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.
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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.
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Bracchi, Luca. "I-eXplainer: applicazione web per spiegazioni interattive." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20424/.
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Lo scopo di questa tesi è dimostrare che è possibile trasformare una spiegazione statica relativa all'output di un algoritmo spiegabile in una spiegazione interattiva con una maggior grado di efficacia. La spiegazione cercata sarà human readable ed esplorabile.
Per dimostrare la tesi userò la spiegazione di uno degli algoritmi spiegabili del toolkit AIX360 e andrò a generarne una statica che chiamerò spiegazione di base. Questa verrà poi espansa ed arricchita grazie a una struttura dati costruita per contenere informazioni formattate e connesse tra loro. Tali informazioni verranno richieste dall'utente, dandogli la possibilità di costruire una spiegazione interattiva che si sviluppa secondo il suo desiderio.
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Håkansson, Marcus, and Frans Biström. "Using Genetic Programming to evolve an AI for StarCraft." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5121.
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This paper is about the possibility to use evolution to make a StarCraft AI better in some areas by using genetic programming. We aimed to use genetic programming to evolve the numbers of squad units, bunkers and turrets, which are an important part of a successful StarCraft AI. We have built a separate application for handling the evolution. This application runs in parallel with StarCraft and modifies files based on the data recieved from a played game. This is good for safety, since if StarCraft crashes the evolution is just stalled not lost. Our tests ran over the course of a few weeks. A combination of a relatively small amount of time, for something very time-consuming, and a lack of experience with genetic programming resulted in a small amount of results. The conclusion is that it is possible to improve an StarCraft AI with genetic programming, however it takes a lot of time.Denna uppsats handlar om möjligheten att använda evolution att göra en StarCraft AI bättre i vissa områden med hjälp av genetisk programmering. Vi siktade på att använda genetisk programmering att utveckla antalet trupp enheter, bunkrar och torn, som är en viktig del av en framgångsrik StarCraft AI.
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Uga, Brenda. "Towards Trustworthy AI : A proposed set of design guidelines for understandable, trustworthy and actionable AI." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385392.
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Artificial intelligence is used today in both everyday applications and specialised expert systems. In situations where relying on the output of the AI brings about the risk of negative consequences, it becomes important to understand why the AI system has produced its output. Previous research in human-computer trust has identified trust antecedents that contribute to formation of trust in an AI artifact, understanding of the system being one of them. In the context of Pipedrive, a sales management system, this thesis investigates how can AI predictions be designed as understandable and trustworthy, and by extension which explanatory aspects provide guidance towards actions to take, and which presentation formats support for- mation of trust. Using a research-through design approach, multiple designs for displaying AI predictions are explored for Pipedrive, leading to a proposal for a set of design guidelines that support understandability, trustworthiness and actionability of AI. Both the designs and the guidelines have been iteratively developed in collaboration with users and design practitioners.Artificiell intelligens används idag både i vardagliga applikationer och expertsystem. I situationer då förtroendet för utdata från AI innebär en risk för negativa konsekvenser blir det viktigt att förstå varför AI-systemet har producerat dess utdata. Tidigare forskning inom människa-datorförtroende har identifierat förtroendeföregångare som bidrar till att skapa förtroende för en AI-artefakt, varav förståelse för systemet är en av dem. Inom ramen för Pipedrive, ett säljhanteringssystem, utreder denna avhandling hur AI-förut-sägelser kan designas på ett förståeligt och pålitligt sätt, och i förlängningen vilka förklarande aspekter som kan ge vägledning gällande de åtgärder som ska vidtas, samt vilka presentationsformat som stödjer skapande av förtroende. Med hjälp av en metod för forskning genom design undersöks flera utföranden för att visa AI-förutsägelser för Pipedrive, vilket leder till ett förslag till en uppsättning riktlinjer för design som stödjer förståelse, pålitlighet och funktionsduglighet. Både design och riktlinjer har utvecklats iterativt i samarbete med användare och designutövare.
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Satish, Likith Poovanna Kelapanda, and Vinay Sudha Ethiraj. "Human-like Super Mario Play using Artificial Potential Fields." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3146.
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Artifi cial potential fi elds is a technique that use attractive and repelling forces to control e.g. robots, or non player characters in games. We show how this technique may be used in a controller for Super Mario in a way create a human-like playing style. By combining fi elds of progression, opponent avoidance and rewards, we get a controller that tries to collect the rewards and avoid the opponents at the same time as it is progressing towards the goal of the level. We use human test persons to improve the controller further by letting them make pair-wise comparisons with human play recordings, and use the feed-back to calibrate the bot for human-like play.Student 1: Likith Poovanna Kelapanda Staish Mob: +46735542609 Student 2: Vinay Sudha Ethiraj Mob: +46736135683
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Yıldız, Melih Burak. "EU’s Proposed AI Regulation in the context of Fundamental Rights : Analysing the Swedish approach through the lens of the principles of good administration." Thesis, Södertörns högskola, Juridik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-46517.
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AI has become one of the most powerful drivers of social change, transforming economies, impacting politics and wars, and reshaping how citizens live and interact. Nevertheless, the implementation of AI can have adverse effects on peoples’ lives. This dissertation first examines the relationship between artificial intelligence and public law, mainly in two domains, administrative law and criminal law. It also provides a clear insight into the potential impact of AI applications on fundamental rights in the legal context of the European Union. Four selected fundamental rights, Human Dignity, Data Protection and Right to Privacy, Equality and Non-discrimination, and Access to Justice, are examined. The dissertation further explores the European Commission's new proposed AI regulation, which was proposed in April 2021. The proposal aims to put forward a risk- based approach for a harmonized EU legislation by considering the ethical and human sides and without unnecessarily restricting the development of AI technologies. The study focuses on examples from Sweden throughout the study and lastly, examines the Swedish approach in the context of the principles of good administration.
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Podder, Tanmay. "ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4757.
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Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.
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Adolfsson, Lovisa. "How will Artificial Intelligence impact the labour market, which jobs will be replaced and what will it mean for society, within the next decade?" Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412832.
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This study examines the impact of Artificial Intelligence on the Labour Market within the next decade. Methods and limitations in the technology and their correlation to work, as well as the possible developments likely to be seen in the coming decade, is presented. It also looks at whether Artificial General Intelligence (a system that meet human performance in all fields) could be invented in the next ten years. So far, methods like machine-, deep- and reinforcement learning has resulted in systems that sometimes exceed human performance but are narrow in skill and proficiency. Meaning that AGI is very unlikely to be achieved before 2030. AI is estimated to replace work in the production-, service-, care- and welfare-, transport-, and warehouse sector. The conclusion, however, is that transformation will happen in a pace such that society will be able manage it without the changes causing mass-unemployment.