The Implementation of Machine Learning in the Game of Checkers Billy Melicher Computer Systems lab 08 2008-2009 1
Abstract Machine learning uses past information to predict future states Can be used in any situation where the past will predict the future Will adapt to situations 2
Introduction Checkers is used to explore machine learning Checkers has many tactical aspects that make it good for studying 3
Background Minimax Heuristics Learning 4
Minimax Method of adversarial search Every pattern(board) can be given a fitness value(heuristic) Each player chooses the outcome that is best for them from the choices they have 5
Minimax 6
Minimax Has exponential growth rate Can only evaluate a certain number of actions into the future – ply 7
Heuristic Heuristics predict out come of a board Fitness value of board, higher value, better outcome Not perfect Requires expertise in the situation to create 8
Heuristics H(s) = c0F0(s) + c1F1(s) + … + cnFn(s) H(s) = heuristic Has many different terms In checkers terms could be: Number of checkers Number of kings Number of checkers on an edge How far checkers are on board 9
Learning by Rote Stores every game played Connects the moves made for each board Relates the moves made from a particular board to the outcome of the board More likely to make moves that result in a win, less likely to make moves resulting in a loss Good in end game, not as good in mid game 10
Learning by Generalization Uses a heuristic function to guide moves Changes the heuristic function after games based on the outcome Good in mid game but not as good in early and end games Requires identifying the features that affect game 11
Development Use of minimax algorithm with alpha beta pruning Use of both learning by Rote and Generalization Temporal difference learning 12
Temporal Difference Learning In temporal difference learning, you adjust the heuristic based on the difference between the heuristic at one time and at another Equilibrium moves toward ideal function U(s) <-- U(s) + α( R(s) + γU(s') - U(s)) 13