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Adaptive Behavior
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
The Stability, Scalability and Performance of Multi-agent Systems
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Issues in Agent Communication: An Introduction
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MDA Explained: The Model Driven Architecture: Practice and Promise
MDA Explained: The Model Driven Architecture: Practice and Promise
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AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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Decision Support Systems
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IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Engineering Applications of Artificial Intelligence
Messor: load-balancing through a swarm of autonomous agents
AP2PC'02 Proceedings of the 1st international conference on Agents and peer-to-peer computing
On the application of clustering techniques to support debugging large-scale multi-agent systems
ProMAS'06 Proceedings of the 4th international conference on Programming multi-agent systems
Using the analytic hierarchy process for evaluating multi-agent system architecture candidates
AOSE'05 Proceedings of the 6th international conference on Agent-Oriented Software Engineering
Intelligent data analysis for the verification of multi-agent systems interactions
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An approach to debug interactions in multi-agent system software tests
Information Sciences: an International Journal
Agent-oriented modeling and development of a system for crisis management
Expert Systems with Applications: An International Journal
Robust design of multi-agent system interactions: A testing approach based on pattern matching
Engineering Applications of Artificial Intelligence
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Communication design is usually complex in Multi-Agent Systems (MAS) because of dynamic emergent behaviours. The lack of proper quantitative measures to assess alternative solutions and guide an iterative development makes this design even harder. The aim of this work is to efficiently find and describe communication patterns that should be avoided in these systems and identify the agents involved in these patterns. For this purpose, this research presents a suite of novel metrics and classification rules that, respectively, measure agents' communication and classify their results to describe patterns. This work also provides tools for automatically measuring the metrics and applying the classification rules. In order to evaluate this work, the results of applying these metrics and classification rules have been compared with the quality attribute of performance in several MAS. Performance is measured as the time between a user's request and the MAS response, and partially represents the factor of the quality of service. The experiments gather four agent-oriented communication designs that belong to two different domains: Crisis-management and Cinema ticket selling. The study reveals that the detected communication patterns are related with performance, and that the proposed metrics can arguably guide the design of communications improving the overall performance of systems.