Bibliografías temáticas / Incremental elicitation

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Literatura académica sobre el tema "Incremental elicitation"

Autor: Grafiati
Publicado: 25 de mayo de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Incremental elicitation"

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Delima, Rosa, and Joko Purwadi. "Development of GoEliTool using Iterative Incremental Approach." CCIT Journal 17, no. 2 (2024): 155–69. https://doi.org/10.33050/ccit.v17i2.2857.

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GoEliTool is a tool for requirements elicitation during the requirements engineering process. Such a tool is specifically developed for elicitation through a goal-oriented requirements engineering (GORE) approach. GoEliTool was developed in the previous study. However, the first version of the tool still had many limitations to be corrected so that the process of data elicitation from stakeholders could be carried out more easily and effectively. In this study, the development of the tool was performed through an iterative, incremental approach based on the limitations found in the previous ve
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Benabbou, Nawal, Cassandre Leroy, Thibaut Lust, and Patrice Perny. "Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (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 soluti
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Benabbou, Nawal, Patrice Perny, and Paolo Viappiani. "Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems." Artificial Intelligence 246 (May 2017): 152–80. http://dx.doi.org/10.1016/j.artint.2017.02.001.

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Benabbou, Nawal, and Patrice Perny. "Interactive resolution of multiobjective combinatorial optimization problems by incremental elicitation of criteria weights." EURO Journal on Decision Processes 6, no. 3-4 (2018): 283–319. http://dx.doi.org/10.1007/s40070-018-0085-4.

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Bourdache, Nadjet, and 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 (July 17, 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 util
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Benabbou, Nawal, Cassandre Leroy, and Thibaut Lust. "An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (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, calle
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Wan, Ping, Chaozhong Wu, Yingzi Lin, and 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 (March 26, 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 increment
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Chu, Thanh-Quang, Alexis Drogoul, Alain Boucher, and Jean-Daniel Zucker. "Interactive Learning of Independent Experts' Criteria for Rescue Simulations." JUCS - Journal of Universal Computer Science 15, no. (13) (2009): 2701–25. https://doi.org/10.3217/jucs-015-13-2701.

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Efficient response to natural disasters has an increasingly important role in limiting the toll on human life and property. The work we have undertaken seeks to improve existing models by building a Decision Support System (DSS) of resource allocation and planning for natural disaster emergencies in urban areas. A multi-agent environment is used to simulate disaster response activities, taking into account geospatial, temporal and rescue organizational information. The problem we address is the acquisition of situated expert knowledge that is used to organize rescue missions. We propose an app
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Schlander, Michael, Diego Hernandez, Oliver Schwarz, and Ramon Schaefer. "PP74 Are Commonly Used Cost-Effectiveness Thresholds Too Low? Empirical Evidence From Economic Studies On The Value Of Life." International Journal of Technology Assessment in Health Care 40, S1 (2024): S85. https://doi.org/10.1017/s0266462324002435.

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IntroductionIn health technology assessment (HTA), “value for money” is frequently conceptualized as incremental cost-effectiveness (CE), with effectiveness measured as (quality-adjusted) life years gained (LYG). Commonly used CE thresholds have been subject to controversial debate. We reviewed and analyzed the worldwide economic literature on the value of a statistical life (VSL), which reported empirical estimates during the last 25 years.MethodsWe conducted an extended systematic literature search in the EconBiz and EconLit databases, spanning the period from January 1995 to December 2020.
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Carr, Katherine, Cam Donaldson, John Wildman, Robert Smith, and 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, no. 4 (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
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Tesis sobre el tema "Incremental elicitation"

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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 déc
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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 ré
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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, bipol
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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
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Libros sobre el tema "Incremental elicitation"

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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"

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

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

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Adam, Loïc, and Sébastien Destercke. "Incremental Elicitation of Preferences: Optimist or Pessimist?" In Algorithmic Decision Theory. 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, and Dimitri Plemenos. "Elicitation of User Preferences via Incremental Learning in a Declarative Modelling Environment." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23960-1_19.

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Rico, Agnès, and Paolo Viappiani. "Incremental Elicitation of Capacities for the Sugeno Integral with a Maximin Approach." In Lecture Notes in Computer Science. 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, and Paolo Viappiani. "Incremental Preference Elicitation in Multi-attribute Domains for Choice and Ranking with the Borda Count." In Lecture Notes in Computer Science. 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, et al. "Integrating Operators’ Preferences into Decisions of Unmanned Aerial Vehicles: Multi-layer Decision Engine and Incremental Preference Elicitation." In Algorithmic Decision Theory. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31489-7_4.

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Benabbou Nawal, Perny Patrice, and Viappiani Paolo. "Incremental Elicitation of Choquet Capacities for Multicriteria Decision Making." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-419-0-87.

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The Choquet integral is one of the most sophisticated and expressive preference models used in decision theory for multicriteria decision making. It performs a weighted aggregation of criterion values using a capacity function assigning a weight to any coalition of criteria, thus enabling positive and/or negative interactions among criteria and covering an important range of possible decision behaviors. However, the specification of the capacity involves many parameters which raises challenging questions, both in terms of elicitation burden and guarantee on the quality of the final recommendat
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Benabbou Nawal and Perny Patrice. "Solving Multi-Agent Knapsack Problems Using Incremental Approval Voting." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-672-9-1318.

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In this paper, we study approval voting for multi-agent knapsack problems under incomplete preference information. The agents consider the same set of feasible knapsacks, implicitly defined by a budget constraint, but they possibly diverge in the utilities they attach to items. Individual utilities being difficult to assess precisely and to compare, we collect approval statements on knapsacks from the agents with the aim of determining the optimal solutions by approval voting. We first propose a search procedure based on mixed-integer programming to explore the space of utilities compatible wi
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Underwood, Mark Alan. "Intranet Exploitation of Social Network Knowledge Intelligence." In Harnessing Social Media as a Knowledge Management Tool. 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
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Actas de conferencias sobre el tema "Incremental elicitation"

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Beauchamp, Aymeric, Thi-Bich-Hanh Dao, Samir Loudni, and Christel Vrain. "Rule-Based Constraint Elicitation For Active Constraint-Incremental Clustering." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00117.

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Bourdache, Nadjet, Patrice Perny, and Olivier Spanjaard. "Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. 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 distri
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Hegazy, S. E., and C. D. Buckingham. "iARRIVE: An Incremental Algorithm for Robust Relative Influence Values Elicitation." In 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." In 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, and Frank Maurer. "Using Acceptance Tests for Incremental Elicitation of Variability in Requirements: An Observational Study." In 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, and Paolo Viappiani. "Incremental Decision Making Under Risk with the Weighted Expected Utility Model." In Twenty-Sixth International Joint Conference on Artificial Intelligence. 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
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Benabbou, Nawal, and Patrice Perny. "Adaptive Elicitation of Preferences under Uncertainty in Sequential Decision Making Problems." In Twenty-Sixth International Joint Conference on Artificial Intelligence. 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
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