There is a plan by the authors for developing a FreeCell solver by supervised deep learning that searches very few positions as compared with solvers currently released. A large number of solutions of high quality to initial positions in FreeCell are required for the supervised deep learning. A FreeCell solver without deep learning is being developed to make the data sets for deep learning. In this paper, construction of the latter FreeCell solver and the distribution of solutions to initial positions of FreeCell obtained by experiments using the solver are described
International audienceThis paper introduces Deep Statistical Solvers (DSS), a new class of trainable...
International audienceThe contribution of this paper is twofold. On the one hand, it introduces a co...
In this article, we describe the lessons learned in creating an efficient solver for the solitaire g...
We evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-n...
We propose an efficient method for determining optimal solutions to such skill-based solitaire card ...
We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and Sliding-...
In recent years, it has been demonstrated that a powerful game AI can be implemented in Go with the ...
"Freecell" is a solitaire game with one deck of cards. The game is played with 8 rows of cards, 4 fr...
In this report, we show how to prune the population size of the Learning Classifier System XCS for c...
. This paper presents an improved algorithm compared to the one given in [7], which finds a minimal ...
Recently, the AlphaGo algorithm has managed to defeat the top level human player in the game of Go. ...
Algorithm selection and generation techniques are two methods that can be used to exploit the perfor...
This paper describes an FPGA implementation of a solution-counting solver for the N-Queens Puzzle. T...
The complexities of various search algorithms are considered in terms of time, space, and cost of so...
This body of work demonstrates how machine learning can be applied to optimize the\ud search for a s...
International audienceThis paper introduces Deep Statistical Solvers (DSS), a new class of trainable...
International audienceThe contribution of this paper is twofold. On the one hand, it introduces a co...
In this article, we describe the lessons learned in creating an efficient solver for the solitaire g...
We evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-n...
We propose an efficient method for determining optimal solutions to such skill-based solitaire card ...
We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and Sliding-...
In recent years, it has been demonstrated that a powerful game AI can be implemented in Go with the ...
"Freecell" is a solitaire game with one deck of cards. The game is played with 8 rows of cards, 4 fr...
In this report, we show how to prune the population size of the Learning Classifier System XCS for c...
. This paper presents an improved algorithm compared to the one given in [7], which finds a minimal ...
Recently, the AlphaGo algorithm has managed to defeat the top level human player in the game of Go. ...
Algorithm selection and generation techniques are two methods that can be used to exploit the perfor...
This paper describes an FPGA implementation of a solution-counting solver for the N-Queens Puzzle. T...
The complexities of various search algorithms are considered in terms of time, space, and cost of so...
This body of work demonstrates how machine learning can be applied to optimize the\ud search for a s...
International audienceThis paper introduces Deep Statistical Solvers (DSS), a new class of trainable...
International audienceThe contribution of this paper is twofold. On the one hand, it introduces a co...
In this article, we describe the lessons learned in creating an efficient solver for the solitaire g...