TY - GEN
T1 - Merging Neural Networks with Traditional Evaluations in Crazyhouse
AU - Makovec, Anei
AU - Pirker, Johanna
AU - Guid, Matej
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - In the intricate landscape of game-playing algorithms, Crazyhouse stands as a complex variant of chess where captured pieces are reintroduced, presenting unique evaluation challenges. This paper explores a hybrid approach that combines traditional evaluation functions with neural network-based evaluations, seeking an optimal balance in performance. Through rigorous experimentation, including self-play, matchups against a variant of the renowned program, Go-deep experiments, and score deviations, we present compelling evidence for the effectiveness of a weighted sum of both evaluations. Remarkably, in our experiments, the combination of 75% neural network and 25% traditional evaluation consistently emerged as the most effective choice. Furthermore, we introduce the use of Best-Change rates, which have previously been associated with evaluation quality, in the context of Monte Carlo tree search-based algorithms.
AB - In the intricate landscape of game-playing algorithms, Crazyhouse stands as a complex variant of chess where captured pieces are reintroduced, presenting unique evaluation challenges. This paper explores a hybrid approach that combines traditional evaluation functions with neural network-based evaluations, seeking an optimal balance in performance. Through rigorous experimentation, including self-play, matchups against a variant of the renowned program, Go-deep experiments, and score deviations, we present compelling evidence for the effectiveness of a weighted sum of both evaluations. Remarkably, in our experiments, the combination of 75% neural network and 25% traditional evaluation consistently emerged as the most effective choice. Furthermore, we introduce the use of Best-Change rates, which have previously been associated with evaluation quality, in the context of Monte Carlo tree search-based algorithms.
KW - Best-Change rates
KW - chess variants
KW - Crazyhouse
KW - heuristic evaluation functions
KW - Monte Carlo tree search
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85187646227&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-54968-7_2
DO - 10.1007/978-3-031-54968-7_2
M3 - Conference paper
AN - SCOPUS:85187646227
SN - 9783031549670
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 15
EP - 25
BT - Advances in Computer Games - 18th International Conference, ACG 2023, Revised Selected Papers
A2 - Hartisch, Michael
A2 - Hsueh, Chu-Hsuan
A2 - Schaeffer, Jonathan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Advances in Computer Games
Y2 - 28 November 2023 through 30 November 2023
ER -