C:/Users/Tom/workspace/Cig-go-follow-up/old/article/cig-go.dvi

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

• Abstract strategy games
• Gomoku
• Irensei
• Artificial neural network
• Pente
• Board game
• Coevolution
• Renju
• Atari

Intelligent Moving of Groups in Real-Time Strategy Games Holger Danielsiek, Raphael St¨uer, Andreas Thom, Nicola Beume, Boris Naujoks, Mike Preuss of a group because every unit shows a unique behavior while the group st

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

• Software
• Artificial life
• Windows games
• Gaming
• Application software
• Flocking
• Boids
• Flock
• Swarm behaviour
• Glest
• Real-time strategy

An Evaluation of Models for Predicting Opponent Positions in First-Person Shooter Video Games Stephen Hladky and Vadim Bulitko filters to predict opponent positions in first-person shooter (FPS) video games. These models

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

• Statistics
• Monte Carlo methods
• Probability theory
• Estimation theory
• Robot control
• Nonparametric statistics
• Markov models
• Particle filter
• Prediction
• Hidden semi-Markov model
• Resampling
• Markov chain

Microsoft Word - Cig Paper New- With Changes 2.doc

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

• Cybernetics
• Applied mathematics
• Evolution
• Cognitive science
• Academia
• Metaheuristics
• Particle swarm optimization
• Evolutionary computation
• Artificial neural network
• Swarm behaviour
• Intelligent agent
• Multi-agent system

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

Learning Position Evaluation for Go with Internal Symmetry Networks Alan Blair, Member, IEEE Abstract— We develop a cellular neural network architecture consisting of a large number of identical neural networks

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

• Artificial neural networks
• Cellular neural network
• Applied mathematics
• Symbol
• Weight
• Mathematics

Intrinsic classification by MML - the Snob program Christopher S. Wallace and David L. Dowe, Department of Computer Science, Monash University, Clayton, Victoria 3168, Australia e-mail: {csw,dld}@cs.monash.edu.au Abstrac

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

Real-time challenge balance in an RTS game using rtNEAT Jacob Kaae Olesen, Georgios N. Yannakakis, Member, IEEE, and John Hallam that NEAT is capable of matching the challenge of the AI agent to the skill of a hard-coded

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

• Game design
• Academia
• Leisure
• Cognition
• Formal sciences
• Artificial intelligence
• Video game development
• Dynamic game difficulty balancing
• Reinforcement learning
• Learning
• Neats and scruffies
• Real-time strategy

A Fuzzy Approach For The 2007 CIG Simulated Car Racing Competition Duc Thang Ho and Jonathan M. Garibaldi Abstract— This paper describes the techniques that have been used by the winning entry of the 2007 IEEE Congress

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

• Systems science
• Systems theory
• Science and technology
• Fuzzy logic
• Control engineering
• Automation
• Cybernetics
• Control theory
• Fuzzy control system
• Fuzzy set
• Controller
• Lotfi A. Zadeh

Social Learning Methods in Board Game Agents Vukosi N. Marivate, Student Member, Tshilidzi Marwala, Senior Member, IEEE this has on the overall performance of the different agents created. This paper focuses on improving

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

• Cognitive science
• Cognition
• Artificial intelligence
• Academia
• Multi-agent systems
• Belief revision
• Complex systems theory
• Simulation
• Intelligent agent
• Reinforcement learning
• Q-learning
• Agent-based model