Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
We introduce PCP-nets, a formalism to model qualitative conditional preferences with probabilistic uncertainty. PCP-nets generalise CP-nets by allowing for uncertainty over the preference orderings. We define and study both optimality and dominance queries in PCP-nets, and we propose a tractable approximation of dominance which we show to be very accurate in our experimental setting. Since PCP-nets can be seen as a way to model a collection of weighted CP-nets, we also explore the use of PCP-nets in a multi-agent context, where individual agents submit CP-nets which are then aggregated into a single PCP-net. We consider various ways to perform such aggregation and we compare them via two notions of scores, based on well known voting theory concepts. Experimental results allow us to identify the aggregation method that better represents the given set of CP-nets and the most efficient dominance procedure to be used in the multi-agent context.
Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
Bing Zhang, Mikio Takeuchi, et al.
NAACL 2025
Fahiem Bacchus, Joseph Y. Halpern, et al.
IJCAI 1995
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks