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Life-and-Death Problem Solver in Go

Author: Byung-Doo LeeDept of Computer Science, Univ. of Auckland

Presented by: Xiaozhen Niu

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Outline Introduction Pattern Clustering Eye Shape Analysis Game Tree Search Conclusions

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Introduction Problem: life-and-death of

groups Major issues:

Infeasible by brute-force search Goal: using heuristic model to

reduce branching factor!

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Basic Components of Solver

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Pattern Clustering Similar life-and-death problems

often have similar solutions (a similar first move to kill or live…)

Group the input patterns into different clusters (no predefined clusters)

Goal: using the first moves of the clusters as the candidate first move

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Pattern Classifier Three clustering methods:

Euclidean distance based Vector product based Kohonen neural network based

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Euclidean distance based Clustering

Calculate distances between the input pattern and the weighted center of each cluster

Find the closest cluster within the range of the threshold P

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Examples

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Vector Product Based Clustering

Calculate similarity degree (cosØ) between instance vector and centroid vector of each cluster

cosØ is 1 => same cosØ is -1 => totally different

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Examples

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Results

Euclidean distance based clustering is the best with lower threshold (<=3)

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Eye Shape Analysis Basic of Eye shape Heuristic influence function

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Eye Shape A surrounded group (A, B, C, D: E)

A: num of points with 4 neighbors, B: with 3 neighbors, C: with 2 neighbors, D: with 1 neighbor

E: Status: Alive, Dead or Unsettled

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Examples

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Heuristic Influence Function Surrounding groups and

surrounded groups both radiate influence to the surrounded area

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Basic Steps 1: Find virtual boundary (radial

sweep algorithm) 2: Calculate influence of surrounding

and surrounded groups 3: calculate the number of neighbors

of zero influence points 4: result point set forms the eye

shape

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Example (1)

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Example (2)

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Results 30 problems (size <=10, not

completely surrounded)

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Game Tree Search Selective alpha-beta search Using pattern clustering and eye

shape analysis to generate a set of first moves

Only in depth 1

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Evaluation Function

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Examples

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Results

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Conclusions Using pattern clustering and eye

shape to do selective search Weaknesses:

similar patterns often has similar first moves to kill, but NOT always!

Eye shape accuracy too low! (36.7%) Size limitation (<=10), not very useful in

real games …