Observer-Aware Probabilistic Planning under Partial Observability

We are interested in planning problems where the agent is aware of the presence of an observer, and where this observer is in a partial observability situation. The agent has to choose its strategy so as to optimize the information transmitted by observations. Building on observer-aware Markov decision processes (OAMDPs), we propose a framework to handle this type of problems and thus formalize properties such as legibility, explicability and predictability. This extension of OAMDPs to partial observability can not only handle more realistic problems, but also permits considering dynamic hidden variables of interest. We discuss theoretical properties of PO-OAMDPs and, experimenting with benchmark problems, we analyze HSVI’s convergence behavior with dedicated initializations and study the resulting strategies.

Authors

• Salomé Lepers Univ. de Lorraine, CNRS, Inria, Loria
• Vincent Thomas
• Olivier Buffet

Keywords

• Probabilistic Planning
• Partial Observability
• Legibility
• Explicability
• Predictability