  Interactive partially observable Markov decision processes (I-POMDP) provide
a formal framework for planning for a self-interested agent in multiagent
settings. An agent operating 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. Traditional 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 number of
agents thereby complicating scalability. In this paper, we model and extend
anonymity and context-specific independence -- problem structures often present
in agent populations -- for computational gain. We empirically demonstrate the
efficiency from exploiting these problem structures by solving a new multiagent
problem involving more than 1,000 agents.
