Extracting Hidden Preferences over Partitions in Hedonic Cooperative Games

The prevalent assumption in hedonic games is that agents are interested solely on the composition of their own coalition. Moreover, agent preferences are usually assumed to be known with certainty. In our work, agents have hidden preferences over partitions. We first put forward the formal definition of hedonic games in partition function form (PFF-HGs), and extend well-studied classes of hedonic games to this setting. Then we exploit three well-known supervised learning models, linear regression, linear regression with basis function, and feed forward neural networks, in order to (approximately) extract the unknown hedonic preference relations over partitions. We conduct a systematic evaluation to compare the performance of these models on PFF-HGs; and, in the process, we develop an evaluation metric specifically designed for our problem. Our experimental results confirm the effectiveness of our work.