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Planning for ad hoc teamwork is challenging because it involves agentscollaborating without any prior coordination or communication. The focus is onprincipled methods for a single agent to cooperate with others. This motivatesinvestigating the ad hoc teamwork problem in the context of individual decisionmaking frameworks. However, individual decision making in multiagent settingsfaces the task of having to reason about other agents' actions, which in turninvolves reasoning about others. An established approximation thatoperationalizes this approach is to bound the infinite nesting from below byintroducing level 0 models. We show that a consequence of the finitely-nestedmodeling is that we may not obtain optimal team solutions in cooperativesettings. We address this limitation by including models at level 0 whosesolutions involve learning. We demonstrate that the learning integrated intoplanning in the context of interactive dynamic influence diagrams facilitatesoptimal team behavior, and is applicable to ad hoc teamwork.

Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams