Detection of Plan Deviation in Multi-Agent Systems
Abstract
Plan monitoring in a collaborative multi-agent system requires an agent to not only monitor the execution of its own plan, but also to detect possible deviations or failures in the plan execution of its teammates. In domains featuring partial observability and uncertainty in the agents’ sensing and actuation, especially where communication among agents is sparse (as a part of a cost-minimized plan), plan monitoring can be a significant challenge. We design an Expectation Maximization (EM) based algorithm for detection of plan deviation of teammates in such a multi-agent system. However, a direct implementation of this algorithm is intractable, so we also design an alternative approach grounded on the agents’ plans, for tractability. We establish its equivalence to the intractable version, and evaluate these techniques in some challenging tasks.
Cite
Text
Banerjee et al. "Detection of Plan Deviation in Multi-Agent Systems." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10134Markdown
[Banerjee et al. "Detection of Plan Deviation in Multi-Agent Systems." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/banerjee2016aaai-detection/) doi:10.1609/AAAI.V30I1.10134BibTeX
@inproceedings{banerjee2016aaai-detection,
title = {{Detection of Plan Deviation in Multi-Agent Systems}},
author = {Banerjee, Bikramjit and Loscalzo, Steven and Thompson, Daniel Lucas},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {2445-2451},
doi = {10.1609/AAAI.V30I1.10134},
url = {https://mlanthology.org/aaai/2016/banerjee2016aaai-detection/}
}