Speaker: Wassila Ouerdane (Laboratoire MICS, Centrale Supélec)
Title: A dialogue game for recommendation with adaptive preference models
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 his responses into account. This feedback can lead to revising the model used to represent the preferences of the decision maker. Our objective in this work is to equip an artificial decision-aider with this adaptive behavior. To do that, we build on decision theory to propose a principle 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 (in the sense that the recommended
option is indeed among the most preferred of the DM).