Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Collaborative plans for complex group action
Artificial Intelligence
QuickSet: multimodal interaction for distributed applications
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Multiple roles, multiple teams, dynamic environment: autonomous Netrek agents
AGENTS '97 Proceedings of the first international conference on Autonomous agents
KQML as an agent communication language
Software agents
Modeling Web sources for information integration
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Needles in a haystack: plan recognition in large spatial domains involving multiple agents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Visualising and debugging distributed multi-agent systems
Proceedings of the third annual conference on Autonomous Agents
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
Varying the user interaction within multi-agent systems
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Role of Acquaintance Models in Agent-Based Production Planning System
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
Adaptive Agent Integration Architectures for Heterogeneous Team Members
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Diagnosis as an Integral Part of Multi-Agent Adaptability TITLE2:
Diagnosis as an Integral Part of Multi-Agent Adaptability TITLE2:
Robust agent teams via socially-attentive monitoring
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Tracking dynamic team activity
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Towards robust teams with many agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Channeled multicast for group communications
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
A Knowledge-Based Approach to Coalition Formation
IEEE Intelligent Systems
Monitoring agents using declarative planning
Fundamenta Informaticae
Interactive execution monitoring of agent teams
Journal of Artificial Intelligence Research
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
On natural language processing and plan recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Abstract architecture for meta-reasoning in multi-agent systems
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Meta-reasoning methods for agent's intention modelling
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Monitoring Agents using Declarative Planning
Fundamenta Informaticae - The 1st International Workshop on Knowledge Representation and Approximate Reasoning (KR&AR)
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Recent years are seeing an increasing need for on-line monitoring of deployed distributed teams of cooperating agents, e.g., for visualization, or performance tracking. However, in deployed systems, we often cannot rely on the agents to communicate their state to the monitoring system: (a) we rarely can change the behavior of already-deployed agents to communicate the required information (e.g., in legacy or proprietary systems); (b) different monitoring goals require different information to be communicated (e.g., agents' beliefs vs. plans); and (c) communications may be expensive, unreliable, or insecure. This paper presents a non-intrusive approach based on plan-recognition, in which the monitored agents' state is inferred from observations of their routine actions. In particular, we focus on inference of the team state based on its observed \emph{routine} communications, exchanged as part of coordinated task execution. The paper includes the following key novel contributions: (i) a \emph{linear time} probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting general knowledge of teamwork to predict agent responses during normal execution, to reduce monitoring uncertainty; and (iii) a monitoring algorithm that trades expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities, to be represented in a single coherent entity. Our empirical evaluation illustrates that monitoring based on observed routine communications enables significant monitoring accuracy, while not being intrusive. The results also demonstrate a key lesson: A combination of complementary low-quality techniques is cheaper, and better, than a single, highly-optimized monitoring approach.