A hierarchical modelling and simulation environment for AI multicomputer design.

Abstract

The mainstream usage of computer applications is expanding from pure data processing to intelligence processing through information processing and knowledge processing. There is increasing demand for high performance computer systems to solve bigger and more complex AI problems. Simulation can offer an efficient means of investigating the enormous number of alternatives for existing or proposed computer architectures, thereby saving effort, time and cost. However, a model developed using the conventional simulation languages is non-modular, not-reusable, inflexible and provides no support for hierarchical decomposition of the system. To enable the hierarchical decomposition of systems and the development of modular, reusable models, object-oriented concepts are required. In this dissertation, an object-oriented modelling and simulation environment using the System Entity Structure (SES) and Discrete Event System Specification (DEVS) formalism is shown to be a powerful, knowledge-based environment for hierarchical modelling and simulation. This knowledge-based simulation environment provides a means for designing complex multiple processor systems. Modelling and simulating the Traveling Salesman Problem using DEVS-Scheme rule-based models with an inference engine and a set of rules is shown. Also centralized, distributed and multilevel control strategies for heuristic search by AI multiagent systems are modelled, simulated, and analyzed. The importance of high bandwidth, high connectivity communications, such as expected from optical devices, is demonstrated. Based on the experiments, a new multilevel computer architecture for artificial intelligence search applications is proposed.