Multipurpose Adversary Planning in the Game of Go Ph.D thesis by Shui Hu Presenter: Ling Zhao Date: November 18, 2002

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

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

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

Basic structures  Hierarchy of objects: group, chain, string, stone  Generate goals: 1. high level goals are generated by static analysis (using knowledge base) 2. low level goals are generated by looking ahead  Goal structure (see example next next page)

Knowledge base

Goal Tree

Goal relations  Goal and subgoals  Master and servant goal

Achievability Black to move, and the goal is to kill white group Achievable: Near-achievable:

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

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)

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.

Example

Goal Tree

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.