1. An Adversarial Planning Approach to Go Paper Authors: S. Willmott, J. Richardson, A. Bundy, J. Levine Presentation Author: A. Botea

2. Outline ● Motivation ● Data Driven Search vs. Goal Driven Driven ● HTN Planning ● Adversarial Planning ● Example ● GOBI - An Adversarial Go Planner ● Experimental Results ● Discussion & Conclusion

3. Motivation ● Classical Heuristic Search: – Successful in games such as Chess, Checkers, Othello... – Not enough in GoSO... ● Other approaches (e.g., planning) should be tried

4. Data & Goals ● Data Driven Search – Normal heuristic search framework – Search tree with game positions as nodes ● Goal Driven Search – Agenda of abstract goals to be achieved – Search is focused on achieving a certain goal – HTN planning (single agent planning) – Adversarial planning (multi-agent planning)

5. Example - Rush Hour ● Data driven approach: – (ID)A* ● Goal driven approach: – Get red car out – Move blue car ● Move green car – Move yellow car ● Move light-blue car –... http://www.eagle-i.com/JAVA/rush.html

6. HTN Planning ● Hierarchical planning approach ● Three types of objects: – Abstract goals – Atomic operators – Plan schemas ● Used to expand abstract goals ● G = G 1 & G 2 & G 3 ● Use objects to build an AND/OR tree

7. HTN Planning Tree ● AND nodes are abstract goals ● OR nodes are schemas ● Operators can modify current world (i.e., change value of low- level features)

8. Adversarial Planning ● Extends HTN planning to a 2- player framework ● Two agents: Alpha & Beta – Each has an agenda of open goals ● Initialized with abstract goals such as win or kill or live; – Take control over resources alternatively – Agents' actions change the world

9. Flow of Resource Control

10. Plan Refinement

11. Contingence Tree

12. Example ● Alpha (black): – Kill-group ● Surround-group & ● Squeeze-space & ● Prevent-eye-formation ● Beta (white): – Save-group ● Make-eye-space ● Make-eyes

13. GOBI - An Adversarial Go Planner ● 1400 lines of code in Common Lisp ● Focused on life & death ● Knowledge base: 45 goals at 5 different abstraction levels ● Test suite: 85 problems from Graded Go Problems for Beginners, vol I – GOBI solves 74% of them

14. Knowldge Base - Example

15. Goal Driven Approach - Advantages ● Representation and communication of domain knowledge – Easy to encode knowledge such as: ● death lies in the hane or ● Don't push along the fifth line ● Search properties – No heuristic evaluator needed – Quiesence is defined automatically – Heuristically bad moves (e.g., sacrifices) are not discriminated ● Search is focused on well defined goals

16. Goal Driven Approach - Disadvantages ● Encoding strategies as goal decompositions is costly ● Hard to express certain knowledge – e.g., how to express patterns in terms of abstract goals? ==> combination of data-driven and goal- driven approaches is a good idea ● Less efficient than Data Driven Approach in domains with shallow search trees

17. Conclusion ● Presented an adversarial planning framework as an alternative to data-driven solving approach ● Application domain: Go ● GOBI - tsume-go planner based on this framework