@inproceedings{54b91361a9b04e9c8f763d99da709134,
title = "Generating beginner heuristics for simple Texas hold'em",
abstract = "Beginner heuristics for a game are simple rules that allow for effective playing. A chain of beginner heuristics of length N is the list of N rules that play the game best. Finding beginner heuristics is useful both for teaching a novice to play the game well and for understanding the dynamics of the game. We present and compare methods for finding beginner heuristics in a simple version of Poker: Pre-Flop Heads-Up Limit Texas Hold'em. We find that genetic programming outperforms greedy-exhaustive search and axis-aligned search in terms of finding well-playing heuristic chains of given length. We also find that there is a limited amount of non-transitivity when playing beginner heuristics of different lengths against each other, suggesting that while simpler heuristics are somewhat general, the more complex seem to overfit their training set.",
keywords = "Empirical study, Games, Genetic programming",
author = "{De Mesentier Silva}, Fernando and Frank Lantz and Julian Togelius and Andy Nealen",
note = "Funding Information: Authors thank the support of CAPES, Coordena{\c c}{\~a}o de Aperfei{\c c}oa-mento de Pessoal de N{\'i}vel Superior - Brazil. Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 ; Conference date: 15-07-2018 Through 19-07-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1145/3205455.3205601",
language = "English (US)",
series = "GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "181--188",
booktitle = "GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference",
}