Agent-Based Knowledge Discovery provides a new technique for performing data-mining over distributed databases. By combining techniques from Distributed AI and Machine Learning, software agents equipped with learning algorithms mine local databases. These agents then co-operate to integrate the knowledge obtained, before presenting the results to the user. We are currently exploring the use of a new software agent language, Agent-K and the application of first order learning techniques to data-mining. However, the main area of investigation is how the agents should interact, and how the knowledge should be integrated.
|Title of host publication||Proceedings of AAAI 1995 Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments|
|Editors||Craig Knoblock, Alon Levy|
|Publication status||Published - 1995|
- machine learning
- intelligent agent
- distributed learning
Davies, W. H. E., & Edwards, P. (1995). Agent-Based Knowledge Discovery. In C. Knoblock, & A. Levy (Eds.), Proceedings of AAAI 1995 Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments (Vol. SS-95-08, pp. 34-37). AAAI Press. https://www.aaai.org/Papers/Symposia/Spring/1995/SS-95-08/SS95-08-007.pdf