Genetic algorithms for mentor-assisted evaluation function optimization
By Omid David, Moshe Koppel, Nathan S. Netanyahu. 2008
18 citations recorded by the source corpus.
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate mentor, we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program with a smaller number of parameters in its evaluation function to mimic the behavior of a superior mentor which uses a more extensive evaluation function. In principle, our mentor-assisted approach could be used in a wide range of problems for which appropriate mentors are available.
Subfields: Artificial Intelligence, Economics and Econometrics
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Citations in the corpus
- Giraffe: Using Deep Reinforcement Learning to Play Chess (2015, methodology): “It is possible to use another stronger chess engine as a mentor to generate ground-truth labels (as has been done for the Falcon project [15]), but for this project, we are aiming to create a system that can learn to play chess with no external help, and as little built-in chess knowledge as…”
- DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess (2016, unknown): “e. In our previous works, we demonstrated how genetic algorithms (GA’s) could be applied successfully to the problem of automatic evaluation function tuning when the features are initialized randomly [3,4,5,6]. Although to the best of our knowledge, these works are the only successful automatic lear”