Reasoning with PCP-nets in a Multi-Agent Context

PCP-nets generalize CP-nets to model conditional preferences with probabilistic uncertainty. In this paper we use PCP-nets in a multiagent 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. We then study two key preference reasoning tasks: 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. Categories and Subject Descriptors I.2.11 [Distributed Artificial Intelligence]: Multiagent systems General Terms Theory, Algorithms, Experimentation Keywords Preference modelling; preference reasoning; multi-agent systems; reasoning with uncertainty.

• Type: Journal
• Year: 2015
• Where: ATAL
• Authors: Cristina Cornelio, Umberto Grandi, Judy Goldsmith, Nicholas Mattei, Francesca Rossi, Kristen Brent Venable
• Keywords: ATAL 2015, Intelligent Agents