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

Shashank Shekhar, Ronen I. Brafman, Guy Shani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageAmerican English
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
Pages11954-11961
Number of pages8
Volume35
ISBN (Electronic)9781713835974
StatePublished - 18 May 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: 2 Feb 20219 Feb 2021

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/02/219/02/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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