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Literatura académica sobre el tema "Incremental elicitation"

Autor: Grafiati

Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Incremental elicitation"

Benabbou, Nawal, Cassandre Leroy, Thibaut Lust y Patrice Perny. "Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 14 (18 de mayo de 2021): 12233–40. http://dx.doi.org/10.1609/aaai.v35i14.17452.

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We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. More precisely, for linear objective (utility) functions, we propose an interactive greedy algorithm interleaving preference queries with the incremental construction of an independent set to obtain an optimal or near-optimal base of a matroid. We also propose an interactive local search algorithm based on sequences of possibly improving exchanges for the same problem. For both algorithms, we provide performance guarantees on the quality of the returned solutions and the number of queries. Our algorithms are tested on the uniform, graphical and scheduling matroids to solve three different problems (committee election, spanning tree, and scheduling problems) and evaluated in terms of computation times, number of queries, and empirical error.

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Benabbou, Nawal, Patrice Perny y Paolo Viappiani. "Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems". Artificial Intelligence 246 (mayo de 2017): 152–80. http://dx.doi.org/10.1016/j.artint.2017.02.001.

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Benabbou, Nawal y Patrice Perny. "Interactive resolution of multiobjective combinatorial optimization problems by incremental elicitation of criteria weights". EURO Journal on Decision Processes 6, n.º 3-4 (12 de mayo de 2018): 283–319. http://dx.doi.org/10.1007/s40070-018-0085-4.

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Bourdache, Nadjet y Patrice Perny. "Active Preference Learning Based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 7741–48. http://dx.doi.org/10.1609/aaai.v33i01.33017741.

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We consider the problem of actively eliciting preferences from a Decision Maker supervising a collective decision process in the context of fair multiagent combinatorial optimization. Individual preferences are supposed to be known and represented by linear utility functions defined on a combinatorial domain and the social utility is defined as a generalized Gini Social evaluation Function (GSF) for the sake of fairness. The GSF is a non-linear aggregation function parameterized by weighting coefficients which allow a fine control of the equity requirement in the aggregation of individual utilities. The paper focuses on the elicitation of these weights by active learning in the context of the fair multiagent knapsack problem. We introduce and compare several incremental decision procedures interleaving an adaptive preference elicitation procedure with a combinatorial optimization algorithm to determine a GSF-optimal solution. We establish an upper bound on the number of queries and provide numerical tests to show the efficiency of the proposed approach.

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Benabbou, Nawal, Cassandre Leroy y Thibaut Lust. "An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 03 (3 de abril de 2020): 2335–42. http://dx.doi.org/10.1609/aaai.v34i03.5612.

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We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinatorial optimization problems with unknown preferences. For the purpose of elicitation, we assume that the decision maker's preferences can be represented by a parameterized scalarizing function but the parameters are initially not known. Instead, the parameter imprecision is progressively reduced by asking preference queries to the decision maker during the search to help identify the best solutions within a population. Our algorithm, called RIGA, can be applied to any multi-objective combinatorial optimization problem provided that the scalarizing function is linear in its parameters and that a (near-)optimal solution can be efficiently determined when preferences are known. Moreover, RIGA runs in polynomial time while asking no more than a polynomial number of queries. For the multi-objective traveling salesman problem, we provide numerical results showing its practical efficiency in terms of number of queries, computation time and gap to optimality.

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Wan, Ping, Chaozhong Wu, Yingzi Lin y Xiaofeng Ma. "Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine". Discrete Dynamics in Nature and Society 2019 (26 de marzo de 2019): 1–17. http://dx.doi.org/10.1155/2019/2745381.

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Driving anger, known as “road rage”, has gradually become a serious traffic psychology issue. Although driving anger identification is solved in some studies, there is still a gap in driving anger grading which is helpful to take different intervening measures for different anger intensity, especially in real traffic environment. The main objectives of this study are: (1) explore a novel driving anger induction method based on various elicitation events, e.g., traffic congestion, vehicles weaving/cutting in line, jaywalking and red light waiting in real traffic environment; (2) apply incremental association Markov blanket (IAMB) algorithm to select typical features related to driving anger states; (3) employ least square support vector machine (LSSVM) to identify different driving anger states based on the selected features. Thirty private car drivers were enrolled to perform field experiments on a busy route selected in Wuhan, China, where drivers’ anger could be induced by the elicitation events within limited time. Meanwhile, three types of data sets including driver physiology, driving behaviors and vehicle motions, were collected by multiple sensors. The results indicate that 13 selected features including skin conductance, relative energy spectrum of β band of electroencephalogram, standard deviation (SD) of pedaling speed of gas pedal, SD of steering wheel angle rate, vehicle speed, SD of speed, SD of forward acceleration and SD of lateral acceleration have significant impact on driving anger states. The IAMB-LSSVM model achieves an accuracy with 82.20% which is 2.03%, 3.15%, 4.34%, 7.84% and 8.36% higher than IAMB using C4.5, NBC, SVM, KNN and BPNN, respectively. The results are beneficial to design driving anger detecting or intervening devices in intelligent human-machine systems.

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Carr, Katherine, Cam Donaldson, John Wildman, Robert Smith y Christopher R. Vernazza. "An Examination of Consistency in the Incremental Approach to Willingness to Pay: Evidence Using Societal Values for NHS Dental Services". Medical Decision Making 41, n.º 4 (18 de marzo de 2021): 465–74. http://dx.doi.org/10.1177/0272989x21996329.

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Introduction Willingness to pay (WTP) is used to generate information about value. However, when comparing 2 or more services using standard WTP techniques, the amounts elicited from participants for the services are often similar, even when individuals state a clear preference for one service over another. An incremental approach has been suggested, in which individuals are asked to first rank interventions and provide a WTP value for their lowest-ranked intervention followed by then asking how much more they are willing to pay for their next preferred choice and so on. To date, evaluation of this approach has disregarded protest responses, which may give information on consistency between stated and implicit rankings. Methods A representative sample of the English population ( n = 790) were asked to value 5 dental services adopting a societal perspective, using a payment vehicle of additional household taxation per year. The sample was randomized to either the standard or the incremental approach. Performance for both methods is assessed on discrimination between values for interventions and consistency between implicit and stated ranks. The data analysis is the first to retain protest responses when considering consistency between ranks. Results The results indicate that neither approach provides values that discriminate between interventions. Retaining protest responses reveals inconsistencies between the stated and implicit ranks are present in both approaches but much reduced in the incremental approach. Conclusion The incremental approach does not improve discrimination between values, yet there is less inconsistency between ranks. The protest responses indicate that objections to giving values to the dental interventions are dependent on a multitude of factors beyond the elicitation process.

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Rehman, Shafiq y Volker Gruhn. "An Effective Security Requirements Engineering Framework for Cyber-Physical Systems". Technologies 6, n.º 3 (12 de julio de 2018): 65. http://dx.doi.org/10.3390/technologies6030065.

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Context and motivation: Cyber-Physical Systems (CPSs) are gaining priority over other systems. The heterogeneity of these systems increases the importance of security. Both the developer and the requirement analyst must consider details of not only the software, but also the hardware perspective, including sensor and network security. Several models for secure software engineering processes have been proposed, but they are limited to software; therefore, to support the processes of security requirements, we need a security requirements framework for CPSs. Question/Problem: Do existing security requirements frameworks fulfil the needs of CPS security requirements? The answer is no; existing security requirements frameworks fail to accommodate security concerns outside of software boundaries. Little or even no attention has been given to sensor, hardware, network, and third party elements during security requirements engineering in different existing frameworks. Principal Ideas/results: We have proposed, applied, and assessed an incremental security requirements evolution approach, which configures the heterogeneous nature of components and their threats in order to generate a secure system. Contribution: The most significant contribution of this paper is to propose a security requirements engineering framework for CPSs that overcomes the issue of security requirements elicitation for heterogeneous CPS components. The proposed framework supports the elicitation of security requirements while considering sensor, receiver protocol, network channel issues, along with software aspects. Furthermore, the proposed CPS framework has been evaluated through a case study, and the results are shown in this paper. The results would provide great support in this research direction.

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Chen, Yangyang, Cong Chen, Hao Wen, Jian-min Jiang, Qiong Zeng, Hongping Shu y Zhong Hong. "From Use Case to Use Case Slices". International Journal of Information System Modeling and Design 10, n.º 4 (octubre de 2019): 24–50. http://dx.doi.org/10.4018/ijismd.2019100102.

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A use case has been widely adopted for requirements specification, which can be created on a high abstraction level or a very detailed level. A use case with a very detailed level can be a lot more useful for requirements elicitation, but such a model may become too complex and difficult to understand the whole context. A use case slice is introduced in order to simplify the requirements analysis and ensure the correctness of software incremental development process (e.g., the agile development process). However, a use case is usually divided into multiple use case slices in a manual way. Some errors may occur during this manual process. In this article, the authors present an automated approach for dividing a use case into use case slices. The approach first decomposes a use case into multiple use case stories, and then these stories can be composed into different use case slices according to different requirements. These use case slices cover all the functionality of the original use case. The authors give a decomposition theory and propose the corresponding algorithm. A case study demonstrates these results.

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Ponsford, Ruth. "“I don’t really care about me, as long as he gets everything he needs” – young women becoming mothers in consumer culture". Young Consumers 15, n.º 3 (12 de agosto de 2014): 251–62. http://dx.doi.org/10.1108/yc-10-2013-00401.

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Purpose – As becoming a mother becomes increasingly embedded in the marketplace, this paper explores how a group of low-income pregnant and newly parenting young mothers engaged with expansive markets for the new mother and baby. Design/methodology/approach – This paper draws on an extended period of fieldwork carried out at a Pupil Referral Unit and a Mother and Baby Unit in the city of Bristol, UK. The research took a staged and incremental approach, incorporating aspects of participant observation, activity-based focus groups and a photo elicitation exercise. Findings – This paper highlights the anxiety the young women experienced around their ability (or lack thereof) to participate in practices of childrearing consumption and details how the young women strived to provide well for their children despite their limited incomes, developing a sophisticated knowledge of markets and adopting a range of budgeting and smart shopping strategies to ensure they could acquire the “stuff” their children “needed”. Originality/value – Contrary to popular discourse, the young women emerge as careful and pragmatic consumers who plan and manage their finances carefully, and the paper acknowledges skills that are often missing from accounts of young mothers and working-class people more broadly.

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Tesis sobre el tema "Incremental elicitation"

Khannoussi, Arwa. "Intégration des préférences d'un opérateur dans les décisions d'un drone autonome et élicitation incrémentale de ces préférences". Thesis, Brest, 2019. http://www.theses.fr/2019BRES0080.

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Un drone totalement autonome est un aéronef sans pilote humain à bord. Il est donc capable d'accomplir une mission sans l'intervention d'un opérateur humain et de prendre des décisions de façon totalement autonome. Cela sous-entend que l'opérateur au sol doit avoir une confiance élevée dans les décisions prises par le drone. L'objectif principal de cette thèse est donc de proposer un moteur de décisions à embarquer dans le drone autonome qui garantit un niveau de confiance élevé de l'opérateur dans la capacité du drone à prendre les "bonnes" décisions. Pour cela nous proposons un moteur de décisions multi-niveaux composé de deux niveaux de décisions principaux. Le premier permet de surveiller l'état du drone et de son environnement pour détecter les événements qui peuvent perturber la réalisation de la mission et déclencher la prise de décision du second niveau. Celui-ci une fois déclenché permet de choisir une action de haut niveau (atterrir, continuer, ...) la mieux adaptée à la situation courante parmi un ensemble d'actions possibles. Ce moteur intègre aussi les préférences d'un opérateur en utilisant des modèles d'Aide Multi-Critère à la Décision. Ces modèles nécessitent une phase en amont de la mission, où les préférences de l'opérateur sont élicitées, avant d'être intégrées dans le drone. Pour réduire l'effort cognitif de l'opérateur pendant cette phase, nous proposons un processus d'élicitation incrémental pendant lequel les questions soumises à l'opérateur sont déduites des réponses précédentes. Cela nous permet de déterminer un modèle représentant fidèlement ses préférences, tout en minimisant le nombre de questions
A fully autonomous unmanned aerial vehicle (UAV) is an aircraft without a human pilot on board. It is consequently able to accomplish a mission without the intervention of a human operator and to make decisions in a totally autonomous way. This implies that the ground operator must have a high level of confidence in the decisions made by the UAV.The main objective of this thesis is therefore to propose a decision engine to be embedded in the autonomous UAV that guarantees a high level of operator confidence in the UAV's ability to make the "right" decisions. For this purpose, we propose a multi-level decision engine composed of two main decision levels. The first one monitors the state of the UAV and its environment to detect events that can disrupt the mission’s execution and trigger the second level. Once triggered, it allows to choose a highlevel action (landing, continuing,...) best adapted to the current situation from a set of possible actions. This engine also integrates the operator's preferences by using Multi-Criteria Decision Aiding models. They require a preliminary phase before the mission, where the operator's preferences are elicited, before being integrated into the UAV. To reduce the operator's effort during this phase, we propose an incremental elicitation process during which the questions submitted to the operator are deduced from the previous answers. This allows us to determine a model that accurately represents his or her preferences, while minimizing the number of questions

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Bourdache, Nadjet. "Élicitation incrémentale des préférences pour l’optimisation multi-objectifs : modèles non-linéaires, domaines combinatoires et approches tolérantes aux erreurs". Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS255.

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Les travaux effectués durant cette thèse s'inscrivent dans le cadre de la théorie de la décision algorithmique, domaine au carrefour de la théorie de la décision, de la recherche opérationnelle et de l'intelligence artificielle. Cette thèse vise à concevoir des méthodes d'optimisation interactive fondées sur l'élicitation incrémentale des préférences pour la prise de décision multicritère, multi-agents ou dans le risque. Nous nous intéressons plus précisément à l'élicitation incrémentale des paramètres de fonctions d'agrégation qui consiste à alterner questions préférentielles permettant de réduire l'incertitude concernant la valeur des paramètres modélisant les préférences particulières du décideur, et exploration de l'espace des solutions, jusqu'à pouvoir déterminer une recommandation de bonne qualité. L'intérêt d'alterner phases de questions et phases d'exploration est double: d'une part, les informations préférentielles récoltées durant une phase d'élicitation permettent de mieux focaliser la phase d'exploration suivante sur les solutions les plus intéressantes pour le décideur; d'autre part, l'exploration de l'espace des solutions permet de guider le choix des questions de manière à ce qu'elles soient les plus informatives possible. Nous introduisons dans cette thèse des méthodes d'élicitation dans différents contextes. Dans un premier temps, nous nous intéressons à des fonctions d'agrégation non-linéaires pour modéliser les préférences du décideur sur un ensemble combinatoire d'alternatives. Nous nous intéressons ensuite à la conception de méthodes d'élicitation prenant en compte la possibilité de la présence d'incohérences dans les réponses du décideur, d'abord sur domaine explicite, puis sur domaine combinatoire. Les algorithmes introduits sont génériques et peuvent s'appliquer à différents problèmes de choix multi-objectifs
This thesis work falls within the area of algorithmic decision theory, a research domain at the crossroad of decision theory, operations research and artificial intelligence. The aim is to produce interactive optimization methods based on incremental preference elicitation in decision problems involving several criteria, opinions of agents or scenarios. Preferences are represented by general decision models whose parameters must be adapted to each decision problem and each decision maker. Our methods interleave the elicitation of parameters and the exploration of the solution space in order to determine the optimal choice for the decision maker. The idea behind this is to use information provided by the elicitation to guide the exploration of the solution space and vice versa. In this thesis, we introduce new incremental elicitation methods for decision making in different contexts : first for decision making in combinatorial domains when the decision models are non-linear, and then in a setting where one takes into account the possibility of inconsistencies in the answers of te decision maker. All the algorithms that we introduce are general and can be applied to a wide range of multiobjective decision problems

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Martin, Hugo. "Optimisation multi-objectifs et élicitation de préférences fondées sur des modèles décisionnels dépendants du rang et des points de référence". Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS101.

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Cette thèse se situe dans le cadre de la théorie de la décision algorithmique, domaine de recherche à l'intersection de la théorie de la décision, de l'intelligence artificielle et de la recherche opérationnelle. Nous nous intéressons à la prise en compte de comportements sophistiqués dans des environnements complexes (décision multicritère, décision collective, décision dans le risque et l'incertain). Nous proposons d'abord des méthodes d'optimisation multi-objectifs sur domaine implicite lorsque les préférences sont représentées par des modèles dépendants du rang (intégrale de Choquet, bipolar OWA, Cumulative Prospect Theory et intégrale de Choquet bipolaire). Ces méthodes reposent sur des approches de programmation mathématique et d'algorithmique discrète. Ensuite, nous présentons des méthodes d'élicitation incrémentale des paramètres de modèles dépendants du rang permettant de prendre en compte la présence d'un point de référence dans les préférences d'un décideur (bipolar OWA, Cumulative Prospect Theory, intégrale de Choquet avec capacités et bicapacités). Finalement, nous abordons la modification structurelle de solutions sous contraintes (coût, qualité) dans des méthodes de tri à plusieurs points de référence. Les différentes approches proposées dans cette thèse ont été testées et nous présentons les résultats numériques obtenus afin d'illustrer leur efficacité pratique
This thesis work falls within the research field of algorithmic decision theory, which is defined at the junction of decision theory, artificial intelligence and operations research. This work focuses on the consideration of sophisticated behaviors in complex decision environments (multicriteria decision making, collective decision making and decision under risk and uncertainty). We first propose methods for multi-objective optimization on implicit sets when preferences are represented by rank-dependent models (Choquet integral, bipolar OWA, Cumulative Prospect Theory and bipolar Choquet integral). These methods are based on mathematical programming and discrete algorithmics approaches. Then, we present methods for the incremental parameter elicitation of rank-dependent model that take into account the presence of a reference point in the decision maker's preferences (bipolar OWA, Cumulative Prospect Theory, Choquet integral with capacities and bicapacities). Finally, we address the structural modification of solutions under constraints (cost, quality) in multiple reference point sorting methods. The different approaches proposed in this thesis have been tested and we present the obtained numerical results to illustrate their practical efficiency

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Kress, Alexander. "An incremental elicitation approach to limited-precision auctions". 2004. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=94950&T=F.

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Friendrich, Wernher Rudolph. "Towards the elicitation of hidden domain factors from clients and users during the design of software systems". Diss., 2008. http://hdl.handle.net/10500/2629.

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This dissertation focuses on how requirements for a new software development system are elicited and what pitfalls could cause a software development project to fail if the said requirements are not captured correctly. A number of existing requirements elicitation methods, namely: JAD (Joint Application Design), RAD (Rapid Application Development), a Formal Specifications Language (Z), Natural Language, UML (Unified Modelling Language) and Prototyping are covered. The aforementioned techniques are then integrated into existing software development life cycle models, such as the Waterfall model, Rapid Prototyping model, Build and Fix model, Spiral model, Incremental model and the V-Process model. Differences in the domains (knowledge and experience of an environment) of a client and that of the software development team are highlighted and this is done diagrammatically using the language of Venn diagrams. The dissertation also refers to a case study highlighting a number of problems during the requirements elicitation process, amongst other the problem of tacit knowledge not surfacing during elicitation. Two new requirements elicitation methodologies are proposed namely: the SRE (Solitary Requirements Elicitation) and the DDI (Developer Domain Interaction) methodology. These two methods could potentially be more time consuming than other existing requirements elicitation methods, but the benefits could outweigh the cost of their implementation, since the new proposed methods have the potential to further facilitate the successful completion of a software development project. Following the introduction of the new requirements elicitation methods, they are then applied to the aforementioned case study and highlight just how the hidden domain of the client may become more visible, because the software development team has gained a deeper understanding of the client’s working environment. They have therefore increased their understanding of how the final product needs to function in order to fulfil the set out requirements correctly. Towards the end of the dissertation a summary and a conclusion as well as future work that could be undertaken in this area are provided.
Computer Science
M. Sc. (Computer Science)

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Libros sobre el tema "Incremental elicitation"

Kress, Alexander. An incremental elicitation approach to limited-precision auctions. 2004.

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Wang, Tianhan. Incremental utility elicitation with the minimax regret decision criterion. 2003.

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Capítulos de libros sobre el tema "Incremental elicitation"

Calbrix, Margot. "Incremental Preference Elicitation for Collective Decision Making". En Algorithmic Decision Theory, 369–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67504-6_29.

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Martin, Hugo y Patrice Perny. "Incremental Preference Elicitation with Bipolar Choquet Integrals". En Algorithmic Decision Theory, 101–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87756-9_7.

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Adam, Loïc y Sébastien Destercke. "Incremental Elicitation of Preferences: Optimist or Pessimist?" En Algorithmic Decision Theory, 71–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87756-9_5.

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Bardis, Georgios, Vassilios Golfinopoulos, Dimitrios Makris, Georgios Miaoulis y Dimitri Plemenos. "Elicitation of User Preferences via Incremental Learning in a Declarative Modelling Environment". En IFIP Advances in Information and Communication Technology, 150–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23960-1_19.

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Rico, Agnès y Paolo Viappiani. "Incremental Elicitation of Capacities for the Sugeno Integral with a Maximin Approach". En Lecture Notes in Computer Science, 156–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58449-8_11.

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Benabbou, Nawal, Serena Di Sabatino Di Diodoro, Patrice Perny y Paolo Viappiani. "Incremental Preference Elicitation in Multi-attribute Domains for Choice and Ranking with the Borda Count". En Lecture Notes in Computer Science, 81–95. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45856-4_6.

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Khannoussi, Arwa, Alexandru-Liviu Olteanu, Christophe Labreuche, Pritesh Narayan, Catherine Dezan, Jean-Philippe Diguet, Jacques Petit-Frère y Patrick Meyer. "Integrating Operators’ Preferences into Decisions of Unmanned Aerial Vehicles: Multi-layer Decision Engine and Incremental Preference Elicitation". En Algorithmic Decision Theory, 49–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31489-7_4.

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Underwood, Mark Alan. "Intranet Exploitation of Social Network Knowledge Intelligence". En Harnessing Social Media as a Knowledge Management Tool, 273–98. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0495-5.ch013.

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Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

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Actas de conferencias sobre el tema "Incremental elicitation"

Bourdache, Nadjet, Patrice Perny y Olivier Spanjaard. "Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/280.

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We introduce a new model-based incremental choice procedure for multicriteria decision support, that interleaves the analysis of the set of alternatives and the elicitation of weighting coefficients that specify the role of criteria in rank-dependent models such as ordered weighted averages (OWA) and Choquet integrals. Starting from a prior distribution on the set of weighting parameters, we propose an adaptive elicitation approach based on the minimization of the expected regret to iteratively generate preference queries. The answers of the Decision Maker are used to revise the current distribution until a solution can be recommended with sufficient confidence. We present numerical tests showing the interest of the proposed approach.

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Hegazy, S. E. y C. D. Buckingham. "iARRIVE: An Incremental Algorithm for Robust Relative Influence Values Elicitation". En 2009 International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED). IEEE, 2009. http://dx.doi.org/10.1109/etelemed.2009.42.

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"A regret-based incremental elicitation for multi-criteria force design". En 24th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2021. http://dx.doi.org/10.36334/modsim.2021.m8.mak-hau.

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Ghanam, Yaser y Frank Maurer. "Using Acceptance Tests for Incremental Elicitation of Variability in Requirements: An Observational Study". En 2011 AGILE Conference. IEEE, 2011. http://dx.doi.org/10.1109/agile.2011.21.

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Gilbert, Hugo, Nawal Benabbou, Patrice Perny, Olivier Spanjaard y Paolo Viappiani. "Incremental Decision Making Under Risk with the Weighted Expected Utility Model". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/640.

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This paper deals with decision making under risk with the Weighted Expected Utility (WEU) model, which is a model generalizing expected utility and providing stronger descriptive possibilities. We address the problem of identifying, within a given set of lotteries, a (near-)optimal solution for a given decision maker consistent with the WEU theory. The WEU model is parameterized by two real-valued functions. We propose here a new incremental elicitation procedure to progressively reduce the imprecision about these functions until a robust decision can be made. We also give experimental results showing the practical efficiency of our method.

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Benabbou, Nawal y Patrice Perny. "Adaptive Elicitation of Preferences under Uncertainty in Sequential Decision Making Problems". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/637.

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This paper aims to introduce an adaptive preference elicitation method for interactive decision support in sequential decision problems. The Decision Maker's preferences are assumed to be representable by an additive utility, initially unknown or imperfectly known. We first study the determination of possibly optimal policies when admissible utilities are imprecisely defined by some linear constraints derived from observed preferences. Then, we introduce a new approach interleaving elicitation of utilities and backward induction to incrementally determine an optimal or near-optimal policy. We propose an interactive algorithm with performance guarantees and describe numerical experiments demonstrating the practical efficiency of our approach.

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