1 life-and-death problem solver in go author: byung-doo lee dept of computer science, univ. of...
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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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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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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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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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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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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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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