Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
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
Agent oriented software engineering with INGENIAS
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
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 evaluation method for multi-agent systems
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Evaluation of multi-agent systems: proposal and validation of a metric plan
Transactions on Computational Collective Intelligence VII
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This paper proposes a corpus of metrics to evaluate the balance of communications in these systems. The hypothesis of this paper is that these metrics are strongly related with the quality of service of the MASs. In addition, some classification rules are provided to classify agents according to the metrics; thus, an origin of the low quality of service of MASs is detected. The detection of this origin is the first step for debugging and improving the communication in MASs. The experimentation of this work uses the INGENIAS Development Kit, because it supports the development of fully functional Multi-agent Systems (MASs) from specification models. As a proof of concept, this work measures two variants of a MAS for managing a crisis situation in a city and shows the relationship between the proposed metrics and the quality of service.