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

Combining Final Score with Winning Percentage by Sigmoid Function in Monte-Carlo Simulations Kazutomo SHIBAHARA Abstract— Monte-Carlo method recently has produced good results in Go. Monte-Carlo Go uses a move which ha

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

• Mathematics
• Game artificial intelligence
• Applied mathematics
• Statistics
• Monte Carlo methods
• Combinatorial game theory
• Monte Carlo tree search
• Stochastic simulation
• Computer Go
• Minimax
• Simulation
• Monte Carlo

Fast collision detection through bounding volume hierarchies in workspace-time space for sampling-based motion planners Ulrich Schwesinger1 , Roland Siegwart1 and Paul Furgale1 Abstract— This paper presents a fast coll

Source URL: europa2.informatik.uni-freiburg.de

• Computer science
• Computational geometry
• 3D computer graphics
• Computing
• Video game development
• Collision detection
• Search algorithms
• Computer graphics
• Motion planning
• Bounding volume
• Collision
• Minimum bounding box

Transiting Areas Patrolled by a Mobile Adversary Ondˇrej Vanˇek, Branislav Boˇsansk´y, Michal Jakob and Michal Pˇechouˇcek which it chooses a closed-walk starting and ending in a given location in the area, termed

Source URL: game.itu.dk

• Game theory
• Pursuit-evasion
• Strategy
• Nash equilibrium
• Search algorithm
• Flow network
• Directed graph
• Best response
• Algorithm

Monte Mario: Platforming with MCTS Emil Juul Jacobsen Rasmus Greve Julian Togelius

Source URL: julian.togelius.com

• Game artificial intelligence
• Monte Carlo tree search
• General game playing
• A* search algorithm
• Tree traversal
• Super Mario
• Artificial intelligence
• Trie
• Tree

Artificial Intelligence–123 Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search ✩ XuanLong Nguyen 1 , Subbarao Kambhampati ∗ , Romeo S. Nigenda Depart

Source URL: rakaposhi.eas.asu.edu

• Mathematics
• Heuristics
• Cognition
• Game artificial intelligence
• Routing algorithms
• Search algorithms
• NP-complete problems
• A* search algorithm
• Applied mathematics
• Admissible heuristic
• Algorithm
• 15 puzzle

Strategy Improvement for Concurrent Reachability and Safety Games ∗ ,† ,‡ Krishnendu Chatterjee† Luca de Alfaro§

Source URL: pub.ist.ac.at

• Mathematics
• Probability
• Game theory
• Game artificial intelligence
• Search algorithms
• Memorylessness
• Determinacy
• Markov decision process
• Reachability
• Strategy
• A* search algorithm
• Stochastic game

Online Evolution for Multi-Action Adversarial Games Niels Justesen1 , Tobias Mahlmann2 , and Julian Togelius3 1 2 IT University of Copenhagen

Source URL: julian.togelius.com

• Mathematics
• Game artificial intelligence
• Search algorithms
• Applied mathematics
• Combinatorial game theory
• Minimax
• Monte Carlo tree search
• Negamax
• Monte Carlo method
• Heuristic
• Greedy algorithm
• A* search algorithm

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

A Risky Proposal: Designing a Risk Game Playing Agent Juan Lozano, Dane Bratz, Abstract Monte Carlo Tree Search methods provide a general framework for modeling decision problems

Source URL: cs229.stanford.edu