Metarules To Improve Tactical Go Knowledge

1. Metarules To Improve Tactical Go Knowledge By Tristan Cazenave Presented by Leaf Wednesday, April 28th, 2004

2. Introduction • Automatic Generation of Rules • Metarules to Reduce the Number of Rules • Experimental Results • Future Work • Conclusion

3. Generate a Rule • Given: a rectangular pattern, and some associated conditions on liberties external to the pattern • Find out: conclusion on the life of strings, or on the eye potential of strings

4. Types of Rules • Rules that conclude on a won state (the goal can always be reached no matter who plays) • Rules that conclude on winning states (the goal can be reached if the friend color plays first)

5. Automatic Generation of Rules • Conditions on external liberties • Generation of rules by retrograde analysis

6. Conditions on External Liberties • Conditions associated to the external liberties are restricted • The restrictions ensure the generated rules are always correct

7. Conditions on External Liberties • Liberties >= 1 means that Black string has at least one liberty outside of the pattern in addition to the internal ones • Liberties if Black >= 1 means that if Black moves there then the resulting string has at least one liberty outside of the pattern in addition to the internal ones

8. In the beginning, generate all possible rules for completely formed eyes For all winning rules, undo White moves to find new won rules For all won rules, undo Black moves to find new winning rules If no winning rule is found in won rules, stop the search Generation of Rules by Retrograde Analysis

9. Metarules to Reduce the Number of Rules • Metarules to suppress subsumed rules • Suppression of rules that can be found dynamically • Suppression of some rules on life given rules on eyes • Suppression of low utility rules

10. Metarules to Suppress Subsumed Rules • When generate a new rule, verify that it’s not a special case of another rule • If the new rule is original, search database for the rule that is special case of the new rule

11. Suppression of Rules that Can be Found Dynamically • Only store won rules in database, and remove winning rules • For all new won rules, undo Black moves to find winning rules, but do not store them • For these winning rules, undo White moves to find won rules, and store them

12. Suppression of some Rules on Life Given Rules on Eyes • If a life rule contains two independent eyes, no need to store it, because it can be deduced from the rules on eye

13. Suppression of Low Utility Rules • If a rule has too many conditions, remove it • If a rule needs too many moves to make life, remove it • Example: (Why?)

14. Experimental Results Repartition of won life rules in the corner Repartition of won life rules on the side

15. Experimental Results Repartition of won eye rules center and side Repartition of won eye rules in the corner

16. Future Work • Using gradual games as a good indicator • Combine other methods like Abstract Proof Search • Use isomers of shapes which is used in the shapes database of D. Dyer

17. Conclusion • Automatically generate rules about lie and eyes • The problem is the size of the database • Use metarules to reduce the number of generated rules