Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability
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.
Cite
Text
Shekhar et al. "Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I13.17420Markdown
[Shekhar et al. "Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/shekhar2021aaai-improved/) doi:10.1609/AAAI.V35I13.17420BibTeX
@inproceedings{shekhar2021aaai-improved,
title = {{Improved Knowledge Modeling and Its Use for Signaling in Multi-Agent Planning with Partial Observability}},
author = {Shekhar, Shashank and Brafman, Ronen I. and Shani, Guy},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {11954-11961},
doi = {10.1609/AAAI.V35I13.17420},
url = {https://mlanthology.org/aaai/2021/shekhar2021aaai-improved/}
}