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Communication Dans Un Congrès Année : 2015

A dialogue game for recommendation with adaptive preference models

Nicolas Maudet

Résumé

To provide convincing recommendations, which can be fully understood and accepted by a decision-maker, a decision-aider must often engage in an interaction and take the decision maker's responses into account. This feedback can lead to revising the model used to represent the preferences of the decision-maker. Our objective in this paper is to equip an artificial decision-aider with this adaptive behavior. To do that, we build on decision theory to propose a principled way to select decision models. Our approach is axiomatic in that it does not only work for a predefined subset of methods---we instead provide the properties that make models compatible with our proposal. Finally, the interaction model is complex since it can involve the exchange of different types of preferential information, as well as others locutions such as justifications. We manage it through a dialogue game, and prove that it satisfies desired properties, in particular termination, and efficiency.
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Dates et versions

hal-01478761 , version 1 (28-02-2017)

Identifiants

  • HAL Id : hal-01478761 , version 1

Citer

Christophe Labreuche, Nicolas Maudet, Wassila Ouerdane, Simon Parsons. A dialogue game for recommendation with adaptive preference models. International Conference on Autonomous Agents and Multiagent systems, May 2015, Istanbul, Turkey. pp.959-967. ⟨hal-01478761⟩
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