ACM SIGSOFT Software Engineering Notes
Combining Inductive and Deductive Inference in Knowledge Management Tasks
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Agent Intelligence Through Data Mining (Multiagent Systems, Artificial Societies, and Simulated Organizations)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A robust agent design for dynamic SCM environments
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
An agent structure for evaluating micro-level MAS performance
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
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As modern applications tend to stretch between large, ever-growing datasets and increasing demand for meaningful content at the user end, more elaborate and sophisticated knowledge extraction technologies are needed. Towards this direction, the inherently contradicting technologies of deductive software agents and inductive data mining have been integrated, in order to address knowledge intensive problems. However, there exists no generalized evaluation methodology for assessing the efficiency of such applications. On the one hand, existing data mining evaluation methods focus only on algorithmic precision, ignoring overall system performance issues. On the other hand, existing systems evaluation techniques are insufficient, as the emergent intelligent behavior of agents introduce unpredictable factors of performance. In this paper, we present a generalized methodology for performance evaluation of intelligent agents that employ knowledge models produced through data mining. The proposed methodology consists of concise steps for selecting appropriate metrics, defining measurement methodologies and aggregating the measured performance indicators into thorough system characterizations. The paper concludes with a demonstration of the proposed methodology to a real world application, in the Supply Chain Management domain.