Knowledge Acquisition from Game Records

Takuya Kojima, Atsushi Yoshikawa

Dept. of Computer Science and Information Engineering National Dong Hwa UniversityReporter ： Lo Jung-Yun

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Outline• Introduction

• A Deductive Approach

• An Evolutionary Approach

• Conclusions

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Introduction

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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

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Classification of Go knowledge

• Classify Go knowledge according to two criteria– Form

• Patterns• Sequence of moves• maxims

– Degree of validity• Strict knowledge• Heuristic knowledge

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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

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A Deductive Approach

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System overview

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Model introduction•Knowledge base

–Basic rules–Forcing rules

•Decision maker

)),,)(*,(()...(: 1 tyxssBCthencondcondifrule mforn

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Rule acquisition algorithm

Chooses good moves to be learned

Extracts relevant parts from board configuration

Generalizes the position and the move

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An Evolutionary Approach

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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

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Algorithm

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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

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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

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Application to Tsume-Go• Compare with two algorithm

– Fixed algorithm

– Semi-fixed algorithm

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Application to Tsume-Go

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Conclusions • Explain 2 approaches:

– Deductive– Evolutionary

• The performance is as good as 1 dan human players