Multipurpose Adversary Planning in the Game of Go

1
Multipurpose Adversary Planning in the Game of Go

• Ph.D thesis by Shui Hu

Presenter Ling Zhao Date November 18, 2002
2
Outline

• Motivations
• Overview
• Basic structures and concepts
• Combining goals
• A concrete example
• Conclusions

3
Motivations

• Go is a strategy game
• Computer Go programs still have difficulties to
  convey humans knowledge
• Multi-purpose moves are quite common in
  human-played Go games
• Traditional multi-valued move is too passive!

4
Overview

• Heuristic adversary planning
• Static analysis and dynamic look ahead
• Look for combined goals and the steps leading to
  them
• Strength actively search for combined goals
• Weakness only a prototype, hard to implement in
  a real system

5
Basic structures

• Hierarchy of objects group, chain, string, stone
• Generate goals
• high level goals are generated by static analysis
  (using knowledge base)
• low level goals are generated by looking ahead
• Goal structure (see example next next page)

6
Knowledge base
7
Goal Tree
8
Goal relations

• Goal and subgoals
• Master and servant goal

9
Achievability
Black to move, and the goal is to kill white group
Achievable
Near-achievable
10
Decide the achievability

• Start from leaf goals
• Generate goal/counter goal pairs
• Use look ahead search
• Propagate results upward
• Note this method can also decide the
  achievability of combined goals

11
CP2 search procedure
g11 (g11, c11) (g11, c12) (g11,c21)
(g11, c33)
g12 (g12, c11) (g12, c12) (g12,c21)
(g12, c33)
g33 (g33, c12) (g33, c12) (g33,c21)
(g33, c33)
12
Interaction of leaf goals

• If the intersection of moves to realize two
  near-achievable goals is not empty, we find some
  multipurpose moves!
• Combine two goals and use the look ahead to
  decide if the multipurpose moves can make one of
  the near-achievable moves achievable.
• If yes, your multipurpose planning works.
• The example explains the situation similar to
  double threats, and there are more situations.

13
Example
14
Goal Tree
15
Goal Tree
16
Conclusions

• A multipurpose planning framework was brought out
• Prototype, can only work on very few finely
  designed example
• Planning is weak, and almost the same as search.