Autonomous Agents and Multi-Agent Systems
Diagnosis of single and multi-agent plans
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Coordinating multiple rovers with interdependent science objectives
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Partial-order planning with concurrent interacting actions
Journal of Artificial Intelligence Research
Supervision and diagnosis of joint actions in multi-agent plans
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Team Cooperation for Plan Recovery in Multi-agent Systems
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
Primary and secondary diagnosis of multi-agent plan execution
Autonomous Agents and Multi-Agent Systems
Monitoring the Execution of a Multi-Agent Plan: Dealing with Partial Observability
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Diagnosis of Simple Temporal Networks
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A decision support system for managing combinatorial problems in container terminals
Knowledge-Based Systems
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The paper introduces and formalizes a distributed approach for the model-based monitoring of the execution of a plan, where concurrent actions are carried on by a team of mobile robots in a partially observable environment. Each robot is monitored on-line by an agent that has the task of tracking all the possible evolutions both under nominal and faulty behavior of the robot and to estimate the belief state at each time instant. The strategy for deriving local solutions which are globally consistent is formalized. The distributed monitoring provides on-line feedback to a system supervisor which has to decide whether building a new plan as a consequence of actions failure. The feasibility of the approach and the gain in the performance are shown by comparing experimental results of the proposed approach with a centralized one.