The Integration of Cognitive Knowledge into Perceptual Representations in Computer Go
Jay Burmeister, Janet Wiles, Helen C. Purchase · 1995
This project targets one facet of Go programming - how to relate simple patterns of stones (e.g., kogeima links) to high level properties (e.g., the outcome of a ladder) for a given context (e.g., the presence or absence of a ladder-breaker stone). The numeric representation generated by a simple influence function provides the representation. The condi- tions under which a ladder is won constitutes knowledge. As Cognitive Scientists, our goal is to design algorithms for integrating cognitive knowledge about Go into perceptual representations which may be used by others to build Go programs. Typical artificial intelligence (AI) approaches to Go programming are based on the translation of perceptual information (modelled by pattern recognition processes) to symbolic form (modelled by rule-based systems) for access by symbolic reasoning processes. In this project we explore the converse process - seeking to integrate cognitive knowledge into a core perceptual representation which can then be accessed by symbolic reasoning processes. The underlying theory is an extension of Hofstadter's theory of high-level perception, originally applied to letter-string analogies in the Copycat project (Chalmers, French and Hofstadter, 1990). We demonstrate the concrete instantiation of our theory in four simulations. The core algorithm we used in the simulations integrated cognitive knowledge by directly modifying the numeric information contained in the perceptual representation of the board. The four simula- tions each implemented different variations of our core algorithm (i.e., static, additive, multi- plicative and context-sensitive modifications of influence values).