Detection of Plan Deviation in Multi-Agent Systems

被引:0
|
作者
Banerjee, Bikramjit [1 ]
Loscalzo, Steven [2 ]
Thompson, Daniel Lucas [1 ]
机构
[1] Univ Southern Mississippi, Sch Comp, Hattiesburg, MS 39402 USA
[2] AFRL Informat Directorate, 26 Elect Pkwy, Rome, NY 13441 USA
来源
THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2016年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:2445 / 2451
页数:7
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