2 articles tagged with #cma-es. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed an evolutionary transfer learning approach to adapt chess AI heuristics for Dragonchess, a 3D chess variant. While direct transfers from Stockfish failed, evolutionary optimization using CMA-ES significantly improved AI performance in this complex multi-layer game environment.
Researchers developed CMA-ES-IG, a new algorithm that helps robots learn user preferences more effectively by incorporating user experience considerations. The algorithm suggests perceptually distinct and informative robot behaviors for users to rank, showing improved scalability, computational efficiency, and user satisfaction compared to existing methods.
