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

Authors

  • Shashank Shekhar Ben Gurion University
  • Ronen I. Brafman Ben Gurion University
  • Guy Shani Ben Gurion University

Keywords:

Planning under Uncertainty

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.

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Published

2021-05-18

How to Cite

Shekhar, S., Brafman, R. I., & Shani, G. (2021). Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11954-11961. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17420

Issue

Section

AAAI Technical Track on Planning, Routing, and Scheduling