Application of Artificial intelligence to Chess Playing Capstone Design Project 2004 Jason Cook Bitboards  Bitboards are 64 bit unsigned integers, with the bits mapped to the 64 squares on the chess board.  It takes 12 bitboards to represent a chess board, one per combination of piece and color.  Bitwise operations have a large speed advantage over 2 dimensional array manipulations, especially on 64 bit hardware where they become a single operation.  Bitwise operations are powerful: left shift by 8 gives the effect of advancing all pawns 1 square, bitwise AND tests for check or capture. Decision Trees  The decision tree is built out of states of the chess board, with the root node being the current state. A decision tree for chess expanded to checkmate on every branch has nodes, so we can never hope to build or evaluate the entire tree.  Evaluation of the decision tree in this project is implemented as an anytime algorithm, which builds part of the tree, then generates and stores a next move based on that tree. If time remains the tree is extended and reevaluated, and the new move replaces the old one. Evaluation Functions  There is one score for each chess board in the decision tree. These are calculated independently of the preceding boards, using criteria borrowed from OSTRICH, a well documented chess engine.  NegaMax (a MiniMax variation) is implemented with recursive descent to bring the score of one terminal node to the root of the decision tree.  Credit for each of your pieces on the board, negative points for your opponent’s pieces, with w eightings per type of piece  Emprise: On which squares could you make a capture in one move, if there we an enemy piece there. This is reduced to a integer value for evaluation purposes.  The engine is predictable. This is a theoretical weakness, but in reality the behavior changes along with the current look ahead allowed by the anytime algorithm. Ideas for further work  Tree pruning algorithm  Evolutionary reasoning experiment to refine settings  GUI, WinBoard/XBoard compatibility, or web interface  Deterministic beginning and/or endgame databases  Make the program “learn” via case based reasoning Root (current board) Bitboards Evaluation score Board after white pawn move Bitboards Evaluation score Board after black knight move Bitboards Evaluation Score Board after white rook move Bitboards Evaluation score... Decision tree implementation  Equal depth breadth first (no pruning of the tree)  Typically built to ~4 ply  Uses dynamic memory allocation, allowed to use up to 90% of physical memory Abstract: Chess presents challenges that are not present in other games, because of its complexity and the number of possible moves to evaluate. A common strategy for chess programs is to develop a tree data structure with all of the possible moves from a given starting board up to a certain depth, and then traverse the tree and evaluate it to find a good move. I have implemented a simple "chess engine", which is the logic behind a full chess computer game. The program attempts to balance as much chess playing power as possible with the constraints of running on a desktop computer. The number of moves evaluated is of great importance in determining the strength of a chess engine of this type. For that reason I have implemented the engine in C to minimize the time of execution for the many tree manipulations and bitwise operations involved. The validation of this project was done by testing the engine against multiple freely available chess engines of varying power. The results of these games were then documented and analyzed. Results  Moderately weak performance against an online chess engine  Elements of intelligence  Indecisive/repeated moves  Opponent appeared to have a begin game database, which allowed it to develop its pieces more effectively.  Visible potential for tuning the AI