Many design problems can be faced with large amount of information and uncertainty that in consequence lead to the large number of problem states, parameters and dependencies between them. Therefore, it is often hardly possible to model the problem in symbolical form using the domain knowledge or to find acceptable solution on the basis of it. In many practical problems there is a requirement for the decision support system to opearte in a dynamically changing environment. The system has to deal with continues data flow, beeing self situated in spatio-temporal environment. In such cases, it could be considered to apply AI techniques and machine learning methods. In this paper we propose an approach that aims to respond to this challenge by the construction of a learning system based on multiagent paradigm. The focus of the paper concentrates on a singleagent level where the local lazy learning method has been analysed. The results of the experiments indicate the satisfactory efficiency of the proposed solution.
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- Lazy Learning of Agent in Dynamic Environment
- Springer Berlin Heidelberg
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