Defining the selective mechanism of problem solving in a distributed system.

Distribution and parallelism are historically important approaches for the implementation of artificial intelligence systems. Research in distributed problem solving considers the approach of solving a particular problem by sharing the problem across a number of cooperatively acting processing agents. Communicating problem solvers can cooperate by exchanging partial solutions to converge on global results. The purpose of this research programme is to make a contribution to the field of Artificial Intelligence by developing a knowledge representation language. The project has attempted to create a computational model using an underlying theory of cognition to address the problem of finding clusters of relevant problem solving agents to provide appropriate partial solutions, which when put together provide the overall solution for a given complex problem. To prove the validity of this approach to problem solving, a model of a distributed production system has been created. A model of a supporting parallel architecture for the proposed distributed production problem solving system (DPSS) is described, along with the mechanism for inference processing. The architecture should offer sufficient computing power to cope with the larger search space required by the knowledge representation, and the required faster methods of processing. The inference engine mechanism, which is a combination of task sharing and result sharing perspectives, is distinguished into three phases of initialising, clustering and integrating. Based on a fitness measure derived to balance the communication and computation for the clusters, new clusters are assembled using genetic operators. The algorithm is also guided by the knowledge expert. A cost model for fitness values has been used, parameterised by computation ration and communication performance. Following the establishment of this knowledge representation scheme and identification of a supporting parallel architecture, a simulation of the array of PEs has been developed to emulate the behaviour of such a system. The thesis reports on findings from a series of tests used to assess its potential gains. The performance of the DPSS has been evaluated to verify the validity of this approach by measuring the gain in speed of execution in a parallel environment as compared with serial processing. The evaluation of test results shows the validity of the proposed approach in constructing large knowledge based systems