Using Adaptive Learning Algorithms to Make Complex Strategic Decisions

Abstract

Traditionally, artificial intelligence (AI) algorithms have not been built on particularly adaptive principles. Systems were created using complex collections of rules that were created specifically for the purpose at hand, and whose flexibility was wholly dependent on what flexibility the programmer incorporated within the rules. As a result, this thesis examines many different algorithms for decision-making, particularly for playing chess. It surveys a number of different techniques for creating a chess-playing system, and finally begins an altered implementation on the genetic algorithm-inspired algorithm that uses Population Dynamics to train a system to understand how to rank board states in a game of chess, which includes more genes than the original algorithm. While still a work in progress, the process of creating the system has already demonstrated some advantages over other algorithms for learning evaluation functions for chess (such as the flexibility of the algorithm), and further work could lead to interesting insight on whether a chess system built using a modified version of Population Dynamics can lead to a system whose skill is comparable to the likes of other chess systems, or even to human players.