Data mining for agent reasoning: a synergy for training intelligent agents
Abstract
The task-oriented nature of data mining (DM) has already been dealt successfully with the employment of intelligent agent systems that distribute tasks, collaborate and synchronize in order to reach their ultimate goal, the extraction of knowledge. A number of sophisticated multi-agent systems (MAS) that perform DM have been developed, proving that agent technology can indeed be used in order to solve DM problems. Looking into the opposite direction though, knowledge extracted through DM has not yet been exploited on MASs. The inductive nature of DM imposes logic limitations and hinders the application of the extracted knowledge on such kind of deductive systems. This problem can be overcome, however, when certain conditions are satisfied a priori. In this paper, we present an approach that takes the relevant limitations and considerations into account and provides a gateway on the way DM techniques can be employed in order to augment agent intelligence. This work demonstrates how the extracted knowledge can be used for the formulation initially, and the improvement, in the long run, of agent reasoning.
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Published as:
A. L. Symeonidis,
K. C. Chatzidimitriou,
I. N. Athanasiadis,
P. A. Mitkas,
Data mining for agent reasoning: a synergy for training intelligent agents,
Engineering Applications of Artificial Intelligence, 20:1097-1111,
2007, doi:10.1016/j.engappai.2007.02.009.
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