1. The Implementation of Machine Learning in the Game of Checkers
Billy Melicher
Computer Systems lab 08 1

2. 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

3. Introduction Checkers is used to explore machine learning
Checkers has many tactical aspects that make it good for studying 3

4. Background
Minimax
Heuristics
Learning 4

5. 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

6. Minimax 6

7. Minimax Has exponential growth rate
Can only evaluate a certain number of actions into the future – ply 7

8. 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

9. 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

10. 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

11. 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

12. Development Use of minimax algorithm with alpha beta pruning
Use of both learning by Rote and Generalization
Temporal difference learning 12

13. 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