Evolutionary Transfer Learning for Dragonchess
This work addresses the challenge of transferring AI knowledge to structurally complex, unexplored game domains, though it is incremental in applying existing evolutionary methods to a new testbed.
The researchers tackled the problem of adapting AI heuristics from chess to Dragonchess, a complex three-dimensional variant, and found that evolutionary optimization significantly improved agent performance, as demonstrated in a 50-round tournament.
Dragonchess, a three-dimensional chess variant introduced by Gary Gygax, presents unique strategic and computational challenges that make it an ideal environment for studying the transfer of artificial intelligence (AI) heuristics across domains. In this work, we introduce Dragonchess as a novel testbed for AI research and provide an open-source, Python-based game engine for community use. Our research investigates evolutionary transfer learning by adapting heuristic evaluation functions directly from Stockfish, a leading chess engine, and subsequently optimizing them using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Initial trials showed that direct heuristic transfers were inadequate due to Dragonchess's distinct multi-layer structure and movement rules. However, evolutionary optimization significantly improved AI agent performance, resulting in superior gameplay demonstrated through empirical evaluation in a 50-round Swiss-style tournament. This research establishes the effectiveness of evolutionary methods in adapting heuristic knowledge to structurally complex, previously unexplored game domains.