A Unified Decision-Theoretic Model for Information Gathering and Communication Planning
Chapter
Accepted version
Åpne
Permanent lenke
https://hdl.handle.net/11250/2783703Utgivelsesdato
2020Metadata
Vis full innførselSamlinger
Originalversjon
10.1109/RO-MAN47096.2020.9223597Sammendrag
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.