Planning in Go Ling Zhao University of Alberta September 15, 2003
Outline Motivations Planning Big picture Current work
Motivations Exhaustive search not longer effective in Go? - very large branching factor - difficulties in evaluation - search tree is very deep - human is daunting
Motivations Solutions: - selective search - embody human knowledge - many other approaches such as neural net - planning systems
Planning? Goal-driving approach different from the traditional data-driven approach Included more or less in many state-of-art programs - Use goals to generate moves - but typically do not check goals later or maintain the goals during several moves
Problems Two frameworks 1. S. Hu: Multipurpose strategic planning 2. S. Willmott: Adversarial planning Not applied to real Computer Go programs. Lots of work to define goals, refine goals and generate moves.
Work to do Embody a planning system in Explorer Expectations (best case) - a module separate from other parts of Explorer - information hidden - useful: better results but not significant overhead
Current work - Dynamic look ahead Problems in Explorer Lack of look ahead - mainly static analysis Implement Minimax + selective search
Example
Other problems Results from selective search can be wrong! How to find a better trade-off?