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Simulation-Based Approach to General Game Playing
TLDR
The aim of General Game Playing (GGP) is to create intelligent agents that automatically learn how to play many different games at an expert level without any human intervention. Expand
CadiaPlayer: A Simulation-Based General Game Player
TLDR
The aim of general game playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. Expand
Checkers Is Solved
TLDR
This paper announces that checkers is now solved: Perfect play by both sides leads to a draw in a game with no mistakes made by either player. Expand
Monte-Carlo Tree Search Solver
TLDR
We introduce a new Monte-Carlo Tree Search variant, called MCTS-Solver, has been designed to play narrow tactical lines better in sudden-death games such as LOA. Expand
N-Grams and the Last-Good-Reply Policy Applied in General Game Playing
TLDR
The aim of general game playing (GGP) is to create programs capable of playing a wide range of different games at an expert level, given only the rules of the game. Expand
Learning Simulation Control in General Game-Playing Agents
TLDR
The aim of General Game Playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human intervention. Expand
Improved Heuristics for Optimal Path-finding on Game Maps
TLDR
We present two effective heuristics for estimating distances between locations in large and complex game maps, both of which reduce the exploration and time complexity of A* search significantly over a standard octile distance metric. Expand
Fringe Search: Beating A* at Pathfinding on Game Maps
TLDR
In this paper, the Fringe Search algorithm is introduced, a new algorithm inspired by the problem of eliminating the inefficiencies with IDA*. Expand
Solving Checkers
TLDR
We present new ideas and algorithms for solving the game of checkers. Expand
Evaluation Function Based Monte-Carlo LOA
TLDR
We investigate how to use a positional evaluation function in a Monte-Carlo simulation-based LOA program (MC-LOA). Expand
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