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No Limit Results MCTS Rules Table 2: Expert Imitator Results (No Limit) against MCTSBot and SimpleBot MCTSBot SimpleBot Average Hyperborean-max 1.7781 ±0.193 0.7935 ±0.084 1.2858 ±0.139 Dynamic 1.3332 ±0.146 0.5928 ±0.058 0.9630 ±0.102 Ensemble 1.3138 ±0.047 0.5453 ±0.058 0.9295 ±0.053 Hyperborean-prob 1.1036 ±0.165 0.5075 ±0.093 0.8055 ±0.129 Sartre-max 0.9313 ±0.<strong>11</strong>7 0.5248 ±0.032 0.7281 ±0.075 Sartre-prob 0.4524 ±0.106 0.3933 ±0.073 0.4228 ±0.090 Tartanian-max 0.1033 ±0.451 0.3450 ±0.087 0.2242 ±0.269 Tartantian-prob -0.0518 ±0.127 0.4221 ±0.032 0.1852 ±0.080 Original Imitator - Decision Policy * Measurements are big blinds per hand
No Limit Results • Dynamic 2nd, Ensemble 3rd • Decision combination not able to do better than single imitator: Hyperborean-max • But, still better than most single imitators • 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 24 and 25: Limit Results Approx. Nash Equilibr
Page 28 and 29: Discussion • Hyperborean-max perf
Page 30: The End.

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