224 citations · 614 across the 33 of their papers we have counts for
5 papers · 2 filters
DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
Eli David, Nathan S. Netanyahu, Lior Wolf
We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of ches…
Genetic Algorithms for Evolving Computer Chess Programs
Eli David, H. Jaap van den Herik, Moshe Koppel +1
This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values…
Expert-Driven Genetic Algorithms for Simulating Evaluation Functions
Eli David, Moshe Koppel, Nathan S. Netanyahu
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 appropria…
Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions
Eli David, H. Jaap van den Herik, Moshe Koppel +1
This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsuper…
Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization
Eli David, Moshe Koppel, Nathan S. Netanyahu
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 appropria…