학술논문

Constructing Optimal Fuzzy Metric Trees for Agent Performance Evaluation
Document Type
Conference
Source
2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on. 2:336-339 Dec, 2008
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Fuzzy sets
Intelligent agent
Multiagent systems
Application software
Organizing
Fuzzy systems
Aggregates
Current measurement
Process design
Testing
Intelligent agents
performance evaluation
fuzzy logic
Language
Abstract
The field of multi-agent systems has reached a significant degree of maturity with respect to frameworks, standards and infrastructures. Focus is now shifted to performance evaluation of real-world applications, in order to quantify the practical benefits and drawbacks of agent systems. Our approach extends current work on generic evaluation methodologies for agents by employing fuzzy weighted trees for organizing evaluation-specific concepts/metrics and linguistic terms to intuitively represent and aggregate measurement information.Furthermore, we introduce meta-metrics that measure the validity and complexity of the contribution of each metric in the overall performance evaluation. These are all incorporated for selecting optimal subsets of metrics and designing the evaluation process incompliance with the demands/restrictions of various evaluation setups, thus minimizing intervention by domain experts. The applicability of the proposed methodology is demonstrated through the evaluation of a real-world test case.