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Artykuły w czasopismach na temat "Incremental elicitation"
Benabbou, Nawal, Cassandre Leroy, Thibaut Lust i Patrice Perny. "Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 14 (18.05.2021): 12233–40. http://dx.doi.org/10.1609/aaai.v35i14.17452.
Pełny tekst źródłaBenabbou, Nawal, Patrice Perny i Paolo Viappiani. "Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems". Artificial Intelligence 246 (maj 2017): 152–80. http://dx.doi.org/10.1016/j.artint.2017.02.001.
Pełny tekst źródłaBenabbou, Nawal, i Patrice Perny. "Interactive resolution of multiobjective combinatorial optimization problems by incremental elicitation of criteria weights". EURO Journal on Decision Processes 6, nr 3-4 (12.05.2018): 283–319. http://dx.doi.org/10.1007/s40070-018-0085-4.
Pełny tekst źródłaBourdache, Nadjet, i 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 (17.07.2019): 7741–48. http://dx.doi.org/10.1609/aaai.v33i01.33017741.
Pełny tekst źródłaBenabbou, Nawal, Cassandre Leroy i Thibaut Lust. "An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 03 (3.04.2020): 2335–42. http://dx.doi.org/10.1609/aaai.v34i03.5612.
Pełny tekst źródłaWan, Ping, Chaozhong Wu, Yingzi Lin i 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 (26.03.2019): 1–17. http://dx.doi.org/10.1155/2019/2745381.
Pełny tekst źródłaCarr, Katherine, Cam Donaldson, John Wildman, Robert Smith i 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, nr 4 (18.03.2021): 465–74. http://dx.doi.org/10.1177/0272989x21996329.
Pełny tekst źródłaRehman, Shafiq, i Volker Gruhn. "An Effective Security Requirements Engineering Framework for Cyber-Physical Systems". Technologies 6, nr 3 (12.07.2018): 65. http://dx.doi.org/10.3390/technologies6030065.
Pełny tekst źródłaChen, Yangyang, Cong Chen, Hao Wen, Jian-min Jiang, Qiong Zeng, Hongping Shu i Zhong Hong. "From Use Case to Use Case Slices". International Journal of Information System Modeling and Design 10, nr 4 (październik 2019): 24–50. http://dx.doi.org/10.4018/ijismd.2019100102.
Pełny tekst źródłaPonsford, 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, nr 3 (12.08.2014): 251–62. http://dx.doi.org/10.1108/yc-10-2013-00401.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaA 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
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.
Pełny tekst źródłaThis 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
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.
Pełny tekst źródłaThis 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
Kress, Alexander. "An incremental elicitation approach to limited-precision auctions". 2004. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=94950&T=F.
Pełny tekst źródłaFriendrich, 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.
Pełny tekst źródłaComputer Science
M. Sc. (Computer Science)
Książki na temat "Incremental elicitation"
Kress, Alexander. An incremental elicitation approach to limited-precision auctions. 2004.
Znajdź pełny tekst źródłaWang, Tianhan. Incremental utility elicitation with the minimax regret decision criterion. 2003.
Znajdź pełny tekst źródłaCzęści książek na temat "Incremental elicitation"
Calbrix, Margot. "Incremental Preference Elicitation for Collective Decision Making". W Algorithmic Decision Theory, 369–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67504-6_29.
Pełny tekst źródłaMartin, Hugo, i Patrice Perny. "Incremental Preference Elicitation with Bipolar Choquet Integrals". W Algorithmic Decision Theory, 101–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87756-9_7.
Pełny tekst źródłaAdam, Loïc, i Sébastien Destercke. "Incremental Elicitation of Preferences: Optimist or Pessimist?" W Algorithmic Decision Theory, 71–85. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87756-9_5.
Pełny tekst źródłaBardis, Georgios, Vassilios Golfinopoulos, Dimitrios Makris, Georgios Miaoulis i Dimitri Plemenos. "Elicitation of User Preferences via Incremental Learning in a Declarative Modelling Environment". W 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.
Pełny tekst źródłaRico, Agnès, i Paolo Viappiani. "Incremental Elicitation of Capacities for the Sugeno Integral with a Maximin Approach". W Lecture Notes in Computer Science, 156–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58449-8_11.
Pełny tekst źródłaBenabbou, Nawal, Serena Di Sabatino Di Diodoro, Patrice Perny i Paolo Viappiani. "Incremental Preference Elicitation in Multi-attribute Domains for Choice and Ranking with the Borda Count". W Lecture Notes in Computer Science, 81–95. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45856-4_6.
Pełny tekst źródłaKhannoussi, Arwa, Alexandru-Liviu Olteanu, Christophe Labreuche, Pritesh Narayan, Catherine Dezan, Jean-Philippe Diguet, Jacques Petit-Frère i Patrick Meyer. "Integrating Operators’ Preferences into Decisions of Unmanned Aerial Vehicles: Multi-layer Decision Engine and Incremental Preference Elicitation". W Algorithmic Decision Theory, 49–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31489-7_4.
Pełny tekst źródłaUnderwood, Mark Alan. "Intranet Exploitation of Social Network Knowledge Intelligence". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Incremental elicitation"
Bourdache, Nadjet, Patrice Perny i Olivier Spanjaard. "Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression". W 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.
Pełny tekst źródłaHegazy, S. E., i C. D. Buckingham. "iARRIVE: An Incremental Algorithm for Robust Relative Influence Values Elicitation". W 2009 International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED). IEEE, 2009. http://dx.doi.org/10.1109/etelemed.2009.42.
Pełny tekst źródła"A regret-based incremental elicitation for multi-criteria force design". W 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.
Pełny tekst źródłaGhanam, Yaser, i Frank Maurer. "Using Acceptance Tests for Incremental Elicitation of Variability in Requirements: An Observational Study". W 2011 AGILE Conference. IEEE, 2011. http://dx.doi.org/10.1109/agile.2011.21.
Pełny tekst źródłaGilbert, Hugo, Nawal Benabbou, Patrice Perny, Olivier Spanjaard i Paolo Viappiani. "Incremental Decision Making Under Risk with the Weighted Expected Utility Model". W 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.
Pełny tekst źródłaBenabbou, Nawal, i Patrice Perny. "Adaptive Elicitation of Preferences under Uncertainty in Sequential Decision Making Problems". W 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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