Boolean Techniques in Discrete Optimization and Expert Systems
Abstract
This thesis is devoted to applying Boolean methods to investigate more efficient methodologies for discrete optimization and expert systems; both are based on "binary decision."
An efficient non-linear 0-1 programming algorithm is proposed, which relies mainly on logic analysis applied to the prime implicants generated interatively from the constraint system. A general method for design optimization with discrete variables is also developed, for which the basis is an accurate neighbourhood search procedure based on Boolean operation.
A new methodology for designing and implementing rule-based expert systems using Boolean methods is proposed. This consists of a concensus-based algorithm for converting a set of rules to a minimal Boolean form, together with a new control algorithm for rapidly minimizing the evidence set required for a solution. These algorithms have considerable potential for simplifying systems, and speeding up the execution, which would be highly desirable for real time systems where high speed is vital.
A procedure for building an expert system on a VLSI chip has been presented. An Erasable Programmable Logic Device (EPLD) is used to "hard wire" the logic rules represented by Boolean expressions on a microchip. The result is an extremely fast system with considerable promise for control applications, and also in other systems where size and speed are important performance characteristics.