A Unified Decision-Theoretic Model for Information Gathering and Communication Planning
Jennifer Renoux (Örebro University)
Tiago S. Veiga (Universidade de Lisboa, Norwegian University of Science and Technology (NTNU))
Pedro U. Lima (Universidade de Lisboa)
Matthijs T.J. Spaan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
We consider the problem of communication planning for human-machine cooperation in stochastic and partially observable environments. Partially Observable Markov Decision Processes with Information Rewards (POMDPs-IR) form a powerful framework for information-gathering tasks in such environments. We propose an extension of the POMDP-IR model, called a Communicating POMDP-IR (com-POMDP-IR), that allows an agent to proactively plan its communication actions by using an approximation of the human’s beliefs. We experimentally demonstrate the capability of our com-POMDPIR agent to limit its communication to relevant information and its robustness to lost messages.