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Active Preference Learning based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem

Abstract : 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 combina-torial 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 parame-terized 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 elici-tation 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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Contributor : Nadjet Bourdache <>
Submitted on : Friday, March 22, 2019 - 2:19:42 PM
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  • HAL Id : hal-02076905, version 1

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Nadjet Bourdache, Patrice Perny. Active Preference Learning based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), Jan 2019, Honolulu, United States. ⟨hal-02076905⟩

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