Transpositions and Move Groups in Monte Carlo Tree Search Benjamin E. Childs, Member, IEEE, James H. Brodeur, and Levente Kocsis. Abstract— Monte Carlo search, and specifically the UCT (Upper Confidence Bounds applied

Source URL: www.csse.uwa.edu.au

• Game artificial intelligence
• Monte Carlo tree search
• Transposition table
• Alphabeta pruning
• Minimax
• Game tree
• Tree traversal
• Computer Go
• Monte Carlo method
• UCT
• Tree
• General game playing

Paranoid Proof-Number Search Jahn-Takeshi Saito and Mark H.M. Winands Abstract— Over the years, an increasing number of twoplayer games has been added to the list of solved games. However, solving multi-player games ha

Source URL: game.itu.dk

• Game artificial intelligence
• Search algorithms
• Combinatorial game theory
• Proof-number search
• Abstract strategy games
• Andor tree
• Alphabeta pruning
• Game tree
• Solved game
• Tree
• B-tree
• Computer chess

Evolving Lose-Checkers Players using Genetic Programming Amit Benbassat and Moshe Sipper Abstract— We present the application of genetic programming (GP) to the zero-sum, deterministic, full-knowledge board game of Los

Source URL: game.itu.dk

• Game artificial intelligence
• Genetic algorithms
• Search algorithms
• Mathematics
• Applied mathematics
• Artificial intelligence
• Mathematical optimization
• Cybernetics
• Minimax
• Alphabeta pruning
• Genetic programming
• Chromosome

A Selective Move Generator for the Game Axis and Allies David L. St-Pierre, Mark H.M. Winands and David A. Watt Abstract— We consider the move generation in a modern board game where the set of all the possible moves i

Source URL: game.itu.dk

• Game artificial intelligence
• Mathematics
• Game theory
• Gaming
• Computer chess
• Alphabeta pruning
• Minimax
• Monte Carlo tree search
• Outline of chess
• Go
• Dynasty Tactics

Reinforcement Learning Techniques in ’Jumper’ MITCHELL BRUNTON and SHAANAN N. COHNEY University of Melbourne General Terms: Machine Learning, Jumper

Source URL: cohney.info

• Game artificial intelligence
• Search algorithms
• Minimax
• Alphabeta pruning
• Principal variation search
• Artificial neural network
• Variation
• Reinforcement learning
• Pruning
• Game tree
• TD-Gammon
• Tree traversal

Pseudocode α–β–pruning AlphaBeta(node, α, β) Input: node ist der aktuell zu behandelnde Knoten / aktueller Zustand die Werte α, β geben das Fenster f¨ ur den Knoten an Output: Wert α f¨

Source URL: www.coga.tu-berlin.de