Reasoning with PCP-net in a Multi-Agent Context: Optimality and Dominance
PCP-nets (Probabilistic CP-nets) generalize CP-nets to model conditional preferences with probabilistic uncertainty. PCP-nets can be used in a multi-agent context to compactly represent a collection of CP-nets, thus using probabilistic uncertainty to reconcile possibly conflicting qualitative preferences expressed by a group of agents. Two key preference reasoning tasks were studied: finding an optimal outcome which best represents the preferences of the agents, and answering dominance queries. Our theoretical and experimental analysis demonstrates that our techniques are efficient and accurate for both reasoning tasks.
PhD student at the Doctoral School in Mathematical Sciences (Computer Science area) of the University of Padova (Italy), under the supervision of Francesca Rossi and in the research group on Preference Reasoning and Aggregation.
Wednesday, 3 December, 2014 (All day)