Developing multi-agent systems with a FIPA-compliant agent framework
Software—Practice & Experience
The Design of Intelligent Agents: A Layered Approach
The Design of Intelligent Agents: A Layered Approach
Debugging multi-agent systems using design artifacts: the case of interaction protocols
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Data visualization for domain exploration: interactive statistical graphics
Handbook of data mining and knowledge discovery
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
ProMAS'06 Proceedings of the 4th international conference on Programming multi-agent systems
Ontology-based test generation for multiagent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Experimental Evaluation of Ontology-Based Test Generation for Multi-agent Systems
Agent-Oriented Software Engineering IX
Evaluation of Multi-Agent System Communication in INGENIAS
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Testing in Agent Oriented Methodologies
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Goal-oriented testing for MASs
International Journal of Agent-Oriented Software Engineering
Detection of undesirable communication patterns in multi-agent systems
Engineering Applications of Artificial Intelligence
Runtime verification of multi-agent systems interaction quality
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Testing interactions in multi-agent systems is a complex task because of several reasons. Agents are distributed and can move through different nodes in a network, so their interactions can occur concurrently and from many different sites. Also, agents are autonomous entities with a variety of possible behaviours, which can evolve during their lives by adapting to changes in the environment and new interaction patterns. Furthermore, the number of agents can vary during system execution, from a few dozens to thousands or more. Therefore, the number of interactions can be huge and it is difficult to follow up their occurrence and relationships. In order to solve these issues we propose the use of a set of data mining tools, the ACLAnalyser, which processes the results of the execution of large scale multi-agent systems in a monitored environment. This has been integrated with an agent development toolset, the INGENIAS Development Kit, in order to facilitate the verification of multi-agent system models at the design level rather than at the programming level.