The Agent-Rule-Class Framework for Multi-Agent Systems

Multi-agent systems have become increasingly mature, but their appearance does not make the traditional OO approach obsolete. On the contrary, OO methodologies can benefit from the principles and tools designed for agent systems. The Agent-Rule-Class (ARC) framework is proposed as an approach that builds agents upon traditional OO system components and makes use of business rules to dictate agent behaviour with the aid of OO components. By modelling agent knowledge in business rules, the proposed paradigm provides a straightforward means to develop agent-oriented systems based on the existing object-oriented systems and offers features that are otherwise difficult to achieve in the original OO systems. The main outcome of using ARC is the achievement of adaptivity. The framework is supported by a tool that ensures agents implement up-to-date requirements from business people, reflecting desired current behaviour, without the need for frequent system rebuilds. ARC is illustrated with a rail track example.

A recursive rule base adjustment algorithm for a fuzzy logic controller

This paper introduces a recursive rule base adjustment to enhance the performance of fuzzy logic controllers. Here the fuzzy controller is constructed on the basis of a decision table (DT), relying on membership functions and fuzzy rules that incorporate heuristic knowledge and operator experience. If the controller performance is not satisfactory, it has previously been suggested that the rule base be altered by combined tuning of membership functions and controller scaling factors. The alternative approach proposed here entails alteration of the fuzzy rule base. The recursive rule base adjustment algorithm proposed in this paper has the benefit that it is computationally more efficient for the generation of a DT, and advantage for online realization. Simulation results are presented to support this thesis. (c) 2005 Elsevier B.V. All rights reserved.