Download Development (cont`d)

The Implementation of Artificial
Intelligence and Temporal
Difference Learning Algorithms in
a Computerized Chess
Programme
By James Mannion
Computer Systems Lab 08-09
Period 3
Abstract

Searching through large sets of data

Complex, vast domains

Heuristic searches

Chess

Evaluation Function

Machine Learning
Introduction

Simple domains, simple heuristics

The domain of chess

Deep Blue – brute force

Looking at 30^6 moves before making the first

Supercomputer

Too many calculations

Not efficient
Introduction (cont’d)

Minimax search

Alpha-beta pruning

Only look 2-3 moves into the future

Estimate strength of position
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Evaluation function

Can improve heuristic by learning
Introduction (cont’d)



Seems simple, but can become quite complex.
Chess masters spend careers learning how to
“evaluate” moves
Purpose: can a computer learn a good
evaluation function?
Background

Claude Shannon, 1950

Brute force would take too long

Discusses evaluation function


2-ply algorithm, but looks further into the future
for moves that could lead to checkmate
Possibility of learning in distant future
Development

Python

Stage 1: Text based chess game

Two humans input their moves

Illegal moves not allowed
Development (cont’d)
Development (cont’d)
Development (cont’d)
Development (cont’d)
•
Stage 2: Introduce a computer player
•
2-3 ply
•
Evaluation function will start out such that
choices are based on a simple piecedifferential where each piece is waited equally
Development (cont’d)

Stage 3: Learning

Temporal Difference Learning

Weight adjustment:

w_i < − − w_i + a((n_ic − n_ip)/(n_ic))

Heuristic function:

h = c_1(p_1) + c_2(p_2) + c_3(p_3) +
c_4(p_4) + c_5(p_5)

Piece values:

p-i = Sum(w_i) – Sum(b_i) over i
Testing


Learning vs No Learning
Two equal, piece-differential players pitted
against each other.

One will have the ability to learn

Thousands of games


Win-loss differential tracked over the length
of the test
By the end, the learner should be winning
significantly more games.
Data
Data (cont'd)
References




Shannon, Claude. “Programming a Computer
for Playing Chess.” 1950
Beal, D.F., Smith, M.C. “Temporal Difference
Learning for Heuristic Search and Game
Playing.” 1999
Moriarty, David E., Miikkulainen, Risto.
“Discovering Complex Othello Strategies
Through Evolutionary Neural Networks.”
Huang, Shiu-li, Lin, Fu-ren. “Using TemporalDifference Learning for Multi-Agent
Bargaining.” 2007