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Limit Results Approx. Nash Equilibrium Exploitive Monte-Carlo Table 1: Expert Imitator Results against Fell Omen 2 and AlistairBot Fell Omen 2 AlistairBot Average Dynamic 0.01342 ±0.006 0.68848 ±0.009 0.35095 ±0.0075 Ensemble 0.00830 ±0.010 0.67348 ±0.010 0.34089 ±0.0100 Rockhopper-max -0.01445 ±0.009 0.69504 ±0.009 0.34030 ±0.0090 PULPO-max -0.00768 ±0.006 0.66053 ±0.024 0.32643 ±0.0150 Sartre-max 0.00825 ±0.014 0.63898 ±0.019 0.32362 ±0.0165 PULPO-prob -0.00355 ±0.0<strong>11</strong> 0.63385 ±0.018 0.31515 ±0.0145 Sartre-prob -0.01816 ±0.006 0.64535 ±0.015 0.31360 ±0.0105 Rockhopper-prob -0.02943 ±0.0<strong>11</strong> 0.63870 ±0.020 0.30464 ±0.0155 Original Imitator - Decision Policy * Measurements are small bets per hand
Limit Results • Dynamic does overall best, followed by Ensemble • Some overlap in standard deviations
Page 1 and 2: On Combining Decisions from Multipl
Page 3 and 4: On Combining Decisions from Multipl
Page 5 and 6: Domain • Two-Player Texas Hold’
Page 7 and 8: How to combine decisions? • Two e
Page 9 and 10: Intransitive Performance Relationsh
Page 11 and 12: Lazy Learners • Particularly suit
Page 13 and 14: BRIEF ARTICLE Expert Imitators BRIE
Page 15 and 16: Decision Policies • Probabilistic
Page 18 and 19: Showdown DIVAT • As in (Johanson,
Page 20 and 21: Experimental Results
Page 22 and 23: Methodology • 6 original imitator
Page 26 and 27: No Limit Results MCTS Rules Table 2
Page 28 and 29: Discussion • Hyperborean-max perf
Page 30: The End.

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