Knowledge Acquisition from Game Records Takuya Kojima, Atsushi Yoshikawa Dept. of Computer Science and Information Engineering National Dong Hwa University Reporter ： Lo Jung-Yun

2 Outline Introduction A Deductive Approach An Evolutionary Approach Conclusions

3 Introduction

4 Purpose The knowledge of human experts has two important features: quality and quantity Some systems have tried to acquire Go knowledge, most of them acquire only fixed-shaped knowledge A new algorithm which yields more flexible knowledge is therefore necessary

5 Classification of Go knowledge Classify Go knowledge according to two criteria –Form Patterns Sequence of moves maxims –Degree of validity Strict knowledge Heuristic knowledge

6 Two Approaches This paper focuses on pattern knowledge Strict Knowledge Heuristic Knowledge Deductive Approach Evolutionary Approach Several rules are acquired from a single training example Acquire a large amount of heuristic knowledge from a large amount of training examples

7 A Deductive Approach

8 System overview

9 Model introduction Knowledge base –Basic rules –Forcing rules Decision maker

10 Rule acquisition algorithm Chooses good moves to be learned Extracts relevant parts from board configuration Generalizes the position and the move

11 An Evolutionary Approach

12 Concept Each rule takes the form of a production rule There are no rules in the initial state Feed, consume, and split –with activation value

13 Algorithm

14 Rules Feeding –When five rules are matched… Consuming –Each rule consumes activation value at each step –Rule whose activation value is 0 die Splitting –If activation value is greater than threshold – split it! Original rules → “parent” Randomly add a new condition from among the objects on the current board

15 Application to Tsume-Go Maybe many rules apply in the same situation –Assign priority Priority assignment algorithm –Assignment of weight to rules –Probability of rule accuracy

16 Application to Tsume-Go Compare with two algorithm –Fixed algorithm –Semi-fixed algorithm

17 Application to Tsume-Go

18 Conclusions Explain 2 approaches: –Deductive –Evolutionary The performance is as good as 1 dan human players