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This paper explores advanced techniques in game tree search, focusing on both deterministic and stochastic games with perfect and imperfect information. It highlights classical algorithms like MiniMax and Alpha-Beta pruning, while introducing advanced search methods and statistical evaluations to enhance performance. The effectiveness of transposition tables, iterative deepening, and various evaluation functions are presented, showcasing a 10% performance improvement with BNS. The study also addresses issues in multi-player games and approximations, offering insights into future research directions.
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Uncertain Reasoning in Games Dmitrijs Rutko Faculty of Computing University of Latvia LU and LMT Computer Science Days at Ratnieki, 2011
Game Tree Search • Deterministic / stochastic games • Perfect / imperfect information games
max 8 min 2 8 max 2 7 8 9 1 2 7 4 3 6 8 9 5 4 √ √ √ Χ Χ √ √ √ Χ Χ Classical algorithms • MiniMax • O(wd) • Alpha-Beta • O(wd/2)
Advanced search techniques • Transposition tables • Time efficiency / high cost of space • PVS • Negascout • NegaC* • SSS* / DUAL* • MTD(f)
max ≥5 min <5 ≥5 max <5 ? ≥5 ≥5 1 2 7 4 3 6 8 9 5 4 √ √ Χ Χ Χ √ Χ √ Χ Χ Uncertain Reasoning • O(wd/2) • More cut-offs
Fmin Fmax FX FX FX FX Game tree analytical evaluation Probability density Cumulativedistribution
Fmin Fmax FX FX FX FX Game tree analytical evaluation
Evaluation function Fish Amount (player) – Fish Amount (opponent)
Conclusions and Future Work • BNS gives a 10 percent performance improvement • Transposition tables • Different evaluation functions • Multi-player game • Approximation search
Questions ? dim_rut@inbox.lv