Parallel applications and solutions in artificial intelligence and expert systems
The work presented in this thesis focuses on the design and implementation of parallel algorithms for problem solving tasks principally in Rule-based Expert Systems and Artificial Intelligence (AI). Rule-based Expert Systems are widely used in AI. Their use covers a wide variety of application areas. However, in most cases, these systems are computation intensive and run slowly. This increases the need for high performance and real-time response. Because of the convergence of parallelism in computer design and the wide spread use of expert system in industry, the design of Parallel Expert System has become of increasing importance. Parallel computation may prove useful in shortening the processing time of the expert systems. Expert systems are being designed for both distributed (loosely-coupled) and shared-memory (tightly-coupled) multiprocessor machines. The work presented here is an attempt to focus on the issues involved in designing a rule-based expert system for a shared memory "multiprocessor system (the Sequent Balance 8000). Eight parallel Forward Chaining models and two parallel Backward Chaining models are implemented. These models are presented in Chapter 5 and 6, together with a study of their efficiency.