Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
A distributed framework for solving the Multiagent Plan Coordination Problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
On the design of coordination diagnosis algorithms for teams of situated agents
Artificial Intelligence
Models and methods for plan diagnosis
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
A distributed control loop for autonomous recovery in a multi-agent plan
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
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The paper addresses the tasks of monitoring and diagnosing the execution of a Multi-Agent Plan, taking into account a very challenging scenario where the degree of system observability may be so low that an agent may not have enough information for univocally determining the outcome of the actions it executes (i.e., pending outcomes). The paper discusses how the ambiguous results of the monitoring step (i.e., trajectory-set) are refined by exploiting the exchange of local interpretations between agents, whose actions are bounded by causal dependencies. The refinement of the trajectory-set becomes an essential step to disambiguate pending outcomes and to explain action failures.