Technology Category
Machine Learning: Transfer, Domain Adaptation, Multi-Task LearningSearch and Optimization: Heuristic SearchMultiagent Systems: Adversarial Agents
Evolutionary Transfer Learning for Dragonchess
🤖 AI Summary
This study addresses the challenge of effectively transferring heuristic knowledge from the chess engine Stockfish to Dragonchess, a three-dimensional board game with substantially different rules and structure. To this end, we introduce Dragonchess as a novel benchmark for AI transfer learning and propose an approach that employs the CMA-ES evolutionary strategy to optimize the heuristic evaluation function after transfer. We also develop an open-source Python engine to facilitate experimentation. In a 50-round Swiss-system tournament, the evolutionarily tuned AI agent significantly outperformed a baseline policy using direct heuristic transfer, demonstrating the feasibility of adapting domain-specific heuristics across dissimilar game environments. This work establishes a new paradigm for transfer learning in complex board games.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Dragonchess
transfer learning
heuristic evaluation
evolutionary optimization
game AI
Innovation
Methods, ideas, or system contributions that make the work stand out.
Evolutionary Transfer Learning
Dragonchess
CMA-ES
Heuristic Adaptation
Game AI
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