Conditional nonlinear planning
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An action language based on causal explanation: preliminary report
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Needles in a haystack: plan recognition in large spatial domains involving multiple agents
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A framework for recognizing multi-agent action from visual evidence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Monitoring deployed agent teams
Proceedings of the fifth international conference on Autonomous agents
Heterogeneous Agent Systems
Declarative problem-solving using the DLV system
Logic-based artificial intelligence
Theoretical and Empirical Aspects of a Planner in a Multi-agent Environment
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
A logic programming approach to knowledge-state planning, II: the DLVk system
Artificial Intelligence
A logic programming approach to knowledge-state planning: Semantics and complexity
ACM Transactions on Computational Logic (TOCL)
Robust agent teams via socially-attentive monitoring
Journal of Artificial Intelligence Research
Interactive execution monitoring of agent teams
Journal of Artificial Intelligence Research
Answer set planning under action costs
Journal of Artificial Intelligence Research
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Tracking dynamic team activity
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Towards fault tolerance for multiagent systems
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Detecting and repairing anomalous evolutions in noisy environments
Annals of Mathematics and Artificial Intelligence
LAIMA: a multi-agent platform using ordered choice logic programming
DALT'05 Proceedings of the Third international conference on Declarative Agent Languages and Technologies
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In this paper we consider the following problem: Given a particular description of a multi-agent system (MAS), is it implemented properly? We assume that we are given (possibly incomplete) information about the system and aim at refuting its proper implementation. In our approach, agent collaboration is described as an action theory. Action sequences reaching the collaboration goal are computed by a planner, whose compliance with the actual MAS behaviour allows to detect possible collaboration failures. The approach can be fruitfully applied to aid in offline testing of a MAS implementation, as well as in online monitoring.