Training Neural networks to play checkers

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Presentation transcript:

Training Neural networks to play checkers Daniel Boonzaaier Supervisor – Adiel Ismail April 2017

Content Project Overview Checkers – the board game Background on Neural Networks Neural Network applied to Checkers Requirements Project Plan References Questions

Project Overview Why this project? A program that teaches itself Checkers Checkers – Complete Information Neural Network

Checkers – the Board Game Two player board game Played on a board sectioned into 64 squares Each player has 12 pieces The goal is to remove all to opponents pieces. Pieces can only move diagonally forward. When a piece reaches the end of the board they are “kinged” and can then move in both directions. Multiple jumps can occur so long as there are corresponding empty spaces for the piece to jump to. If an opportunity to jump is present it must be taken.

Background on Neural Networks What is a Neural Network? Machine Learning Algorithm. Input propagates through the network to get an output. An error is gained from output with what is expected. Update network by going back through the network. Update till error is acceptably small.

Background on Neural Networks Has an input, a hidden and an output layer. Nodes are fully connected by weights. Calculation in the hidden layer to produce an output. Training is done on a set of inputs with back propagation used to update weights. Update weights till error is acceptably small. Once trained the weights are fixed for testing.

Neural Network applied to Checkers Checkers board will be represented by the 32 places on the board where a piece can be placed. The 32 places will be the inputs. Calculate scores of all possible moves. Move with highest score should be chosen.

Requirements Learn from just given rules with no external help. Neural network must not be over trained. Play checkers to at least average human competency.

Project Plan Term 1 – Research Term 2 – Designing and Prototyping Research related works and determine requirements Term 2 – Designing and Prototyping Design program and neural net Term 3 – Implementation of Design Create program and train neural net Term 4 – Testing and Refining Test competency and refine program if necessary

References A. L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers”, IBM Journal, Vol 3, No. 3, (July 1959) K. Chellapilla, D. B. Fogel, “Evolving Neural Networks to Play Checkers Without Relying on Expert Knowledge”, IEEE Transactions on Neural Networks, Vol. 10, No 6, (Nov 1999) N. Franken, A. P. Engelbrecht, “Evolving intelligent game-playing agents”, Proceedings of SAICSIT, Pages 102-110, (2003) N. Franken, A. P. Engelbrecht, “Comparing PSO structures to learn the game of checkers from zero knowledge”, The 2003 Congress on Evolutionary Computation. (2003) A. Singh, K. Deep, “Use of Evolutionary Algorithms to Play the Game of Checkers: Historical Developments, Challenges and Future Prospects”, Proceedings of the Third International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 259, (2014)

Questions?