A unifying framework for observer-aware planning and its complexity

@InProceedings{pmlr-v161-miura21a,
  title = 	 {A unifying framework for observer-aware planning and its complexity},
  author =       {Miura, Shuwa and Zilberstein, Shlomo},
  booktitle = 	 {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {610--620},
  year = 	 {2021},
  editor = 	 {de Campos, Cassio and Maathuis, Marloes H.},
  volume = 	 {161},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {27--30 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v161/miura21a/miura21a.pdf},
  url = 	 {https://proceedings.mlr.press/v161/miura21a.html},
  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.}
}