Integrating multiple problem solvers in knowledge-based systems

Abstract

Research in knowledge-based systems has shown that the use of multiple knowledge representation formalisms and reasoning mechanisms for achieving a specific task in complex domains could result in efficient and effective problem solving. This has led to the development of a number of general architectures and application-specific systems integrating multiple problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge representation formalism on which the inference mechanism is working to achieve the (sub)task. A knowledge-based system making use of different problem solvers should address a number of critical aspects of integration of the solvers, like interaction, invocation, reactiveness, learning and expandability. The aim of this paper is to distinguish and discuss essential integration aspects, and to review a number of proposed general hybrid architectures and application specific hybrid systems on the basis of these aspects. The review shows that none of these general architectures or application specific systems directly addresses all the identified integration aspects. In general, the limitations exhibited by these systems are due to the naive form of interaction and invocation of the integrated solvers. We give a high level specification for a competent hybrid knowledge-based architecture that supports the identified integration aspects.