A Unifying Framework for Observer-Aware Planning and Its Complexity

Abstract

Being aware of observers and the inferences they make about an agent’s behavior is crucial for successful multi-agent interaction. Existing works on observer-aware planning use different assumptions and techniques to produce observer-aware behaviors. We argue that observer-aware planning, in its most general form, can be modeled as an Interactive POMDP (I-POMDP), which requires complex modeling and is hard to solve. Hence, we introduce a less complex framework for producing observer-aware behaviors called Observer-Aware MDP (OAMDP) and analyze its relationship to I-POMDP. We establish the complexity of OAMDPs and show that they can improve interpretability of agent behaviors in several scenarios.