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METAGAMER: An Agent for Learning and Planning in General Games

METAGAMER: An Agent for Learning and Planning in General Games . Barney Pell NASA Ames Research Center. OUTLINE OF TALK. METAGAME Chess-Like Games and Generation METAGAMER Performance Related Work Implications for learning and reasoning in games Conclusion. Knight-Zone Chess.

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METAGAMER: An Agent for Learning and Planning in General Games

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  1. METAGAMER: An Agent for Learning and Planning in General Games Barney Pell NASA Ames Research Center

  2. OUTLINE OF TALK • METAGAME • Chess-Like Games and Generation • METAGAMER • Performance • Related Work • Implications for learning and reasoning in games • Conclusion

  3. Knight-Zone Chess

  4. META-GAME PLAYING • Diverse Class of Games • Automated Game Designer • Uniform Representation • Programs Must Analyze Rules • No Existing Experts • Evaluation by Metagame Tournament • Increase challenge by extending class over time

  5. TOURNAMENT FORMAT • Accept Rules • Initial Analysis • Individual Contests • Post-Mortem Analysis • Time Limits • No Programmer Modification • Winner

  6. Computer Game-Playing Research General game knowledge Resource bounds Class of games (Heavy use today) Game rules Specific game knowledge Player (Minimal use today) Opponent Player Competitive context

  7. Computer Game-Playing Research General game knowledge Resource bounds Class of games Game rules Specific game knowledge Player Opponent Player Competitive context

  8. Computer Game-Playing Research General game knowledge Resource bounds Class of games Game rules Specific game knowledge Player Opponent Player Competitive context

  9. Meta-Game-Playing Research General game knowledge Resource bounds Class of games Game generator Game rules Game rules Specific game knowledge Game rules Metagamer Opponent Metagamer Player Opponent Player Competitive context

  10. Class and Generator • Symmetric Chess-Like Games • Global Symmetry • Board • Pieces • Initial Setup • Goals • Includes many known games of varying complexity • Game Generator • Stochastic Context-Free Generation • Controllable Parameters • Generates some interesting games

  11. METAGAMER • Class and Strategy in General Representation • Game-Specializer: Compiles to Improve Efficiency • Game-Analyzer: Produces Specialized Analysis Tables • Advisors: Use Analysis Tables to Evaluate Position • Weights • Relative Importance of General Advisors • Tuned by experiments • Values not as crucial as for base-level • Search Engine: Alpha-Beta Minimax

  12. Advisors for Chess-Like Games • Mobility • dynamic-mobility • static-mobility • capturing-mobility • eventual-mobility • Threats and Capturing • global-threats • potent-threats • possession • Goals and Step Functions • Vital • arrival-distance • promote-distance

  13. Results in Competition • Checkers • Stronger than Greedy-Material • 1-man handicap ==> draws strong opponent • Strong if 1-man handicap • Chess • Stronger than Greedy-Material • Can Defeat Human Novices • Good Positional Play, Weak Tactics • Other games • Chinese chess, Japanese chess, Chess variations: “Sensible play” • Generated Games (w/o human assistance) • All Advisors ==> won Tourney • No Version was best on every game • Knowledge outperforms Search (so far!) • "Rediscovers" Known Strategies • Long-range strategic capabilities with limited search • Learning Gives Improvement

  14. Related work • Other work in learning and planning games • Forks, abstraction, parameter-learning, feature-learning and generation • Metagamer works on unknown games • Does not rely on strong opponents • Benefits from Rules • Plays Entire Game

  15. Implications for learning and planning in general games • Game analysis like scientific investigation • Intellectual development • Discipline for perceiving, searching, reacting, time mgmt • Practice and training • Progression of skills • Multi-strategy approaches • Constraint-based design • Theorems and lemmas • Analogies • Theory-driven experiments • Exploration and Trial and error • Cultural • Transfer of knowledge • Authorship and history

  16. CONCLUSION • Metagame reveals wide open problems • Attractive properties as evaluation testbed • Competitive performance criteria • Quantifiable demonstration of generality • Requires learning and reasoning on integrated problems • Humans have high competence, so impressive if programs could play well • Increasing challenges over time • More general classes of problems (eg chess + go) • Larger scale problems (bigger boards, more pieces) • More complex domain attributes (eg multi-player, incomplete information, chance) • Chess-Like Games is a good start • Existence proof that something is possible here • Hard problem (little improvement in 10 years!) • Workbench makes development easy • Similar ideas could be applied to other challenges • Eg. planning, categorization, robotics competitions • Key to any of these • Quantify claims of generality to the information available to humans in system • Removing information forces new challenges for agents

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