Individual Planning in Agent Populations: Exploiting Anonymity and

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent oper-ating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Tradi-tional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the num-ber of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific indepen-dence – problem structures often present in agent populations – for computational gain. We empirically demonstrate the effi-ciency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents