Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability

Shashank Shekhar; Ronen I. Brafman; Guy Shani

AAAI 2021technical5 citations

Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability

Shashank Shekhar, Ronen I. Brafman, Guy Shani

Abstract

Collaborative Multi-Agent Planning (MAP) problems with uncertainty and partial observability are often modeled as Dec-POMDPs. Yet, in deterministic domains, Qualitative Dec-POMDPs can scale up to much larger problem sizes. The best current QDec solver (QDec-FP) reduces MAP problems to multiple single-agent problems. In this paper, we describe a planner that uses richer information about agents’ knowledge to improve upon QDec-FP. With this change, the planner not only scales up to larger problems with more objects, but it can also support signaling, where agents signal information to each other by changing the state of the world.

BibTeX

@inproceedings{aaai2021_improvedknowledg,
  title = {Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability},
  author = {Shashank Shekhar and Ronen I. Brafman and Guy Shani},
  booktitle = {AAAI 2021},
  year = {2021}
}