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ORCID1st CIG 2005: Colchester, Essex, UK
- Proceedings of the 2005 IEEE Symposium on Computational Intelligence and Games (CIG05), Essex University, Colchester, Essex, UK, 4-6 April, 2005. IEEE 2005
- Georgios N. Yannakakis, John Hallam:
A Generic Approach for Generating Interesting Interactive Pac-Man Opponents. - Peter Blackburn, Barry O'Sullivan:
Building Reactive Characters for Dynamic Gaming Environments. - Leslie Luthi, Mario Giacobini, Marco Tomassini:
Synchronous and Asynchronous Network Evolution in a Population of Stubborn Prisoners. - Jörg Denzinger, Chris Winder:
Combining Coaching and Learning to Create Cooperative Character Behavior. - Kenneth O. Stanley, Bobby D. Bryant, Risto Miikkulainen:
Real-Time Evolution in the NERO Video Game (Winner of CIG 2005 Best Paper Award). - Umberto Cerruti, Mario Giacobini, Ugo Merlone:
A New Framework to Analyze Evolutionary 2 x 2 Symmetric Games. - Jin-Hyuk Hong, Sung-Bae Cho:
Evolving Reactive NPCs for the Real-Time Simulation Game. - Kazutomo Shibahara, Nobuo Inui, Yoshiyuki Kotani:
Adaptive Strategies of MTD-f for Actual Games. - Bruno Bouzy, Guillaume Chaslot:
Bayesian Generation and Integration of K-nearest-neighbor Patterns for 19x19 Go. - Tristan Cazenave, Bernard Helmstetter:
Combining Tactical Search and Monte-Carlo in the Game of Go. - Nanlin Jin, Edward P. K. Tsang:
Co-evolutionary Strategies for an Alternating-Offer Bargaining Problem. - Jörg Denzinger, Kevin Loose, Darryl Gates, John W. Buchanan:
Dealing with Parameterized Actions in Behavior Testing of Commercial Computer Games. - Akihiro Kishimoto, Martin Müller:
Dynamic Decomposition Search: A Divide and Conquer Approach and its Application to the One-Eye Problem in Go. - Evangelos Papacostantis, Andries P. Engelbrecht, Nelis Franken:
Coevolving Probabilistic Game Playing Agents using Particle Swarm Optimization Algorithm. - Colin Frayn:
An Evolutionary Approach to Strategies for the Game of Monopoly. - Yngvi Björnsson, Markus Enzenberger, Robert C. Holte, Jonathan Schaeffer:
Fringe Search: Beating A* at Pathfinding on Game Maps. - Maria Fasli, Michael Michalakopoulos:
Designing and Implementing E-market Games. - Jelle R. Kok, Pieter Jan't Hoen, Bram Bakker, Nikos Vlassis:
Utile Coordination: Learning Interdependencies Among Cooperative Agents. - Jay Bradley, Gillian Hayes:
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions. - Abdennour El Rhalibi, Michael Burkey:
A Hybrid AI System for Agent Adaptation in a First Person Shooter. - Aliza Gold:
Academic AI and Videogames: A Case Study of Incorporating Innovative Academic Research Into a Videogame Prototype. - Michael Chung, Michael Buro, Jonathan Schaeffer:
Monte Carlo Planning in RTS Games. - Daniel Livingstone:
Coevolution in Hierarchical AI for Strategy Games. - Julian Togelius, Simon M. Lucas:
Forcing Neurocontrollers to Exploit Sensory Symmetry Through Hard-wired Modularity in the Game of Cellz. - Simon M. Lucas:
Evolving a Neural Network Location Evaluator to Play Ms. Pac-Man. - Peter I. Cowling:
Board Evaluation For The Virus Game. - David B. Fogel, Timothy J. Hays, Sarah L. Hahn, James Quon:
Further Evolution of a Self-Learning Chess Program. - Chris Miles, Sushil J. Louis:
Case-Injection Improves Response Time for a Real-Time Strategy Game.
Posters
- Kenneth Chisholm, Donald Fleming:
A Study of Machine Learning using the Game of Fox and Geese. - Damien Devigne, Philippe Mathieu, Jean-Christophe Routier:
Team of cognitive agents with a leader: how to let them acquire autonomy. - Erfu Yang, Dongbing Gu:
A Survey on Multiagent Reinforcement Learning Towards Multi-Robot Systems. - Kevin Burns:
Pared-down Poker: Cutting to the Core of Command and Control. - Kevin Burns:
On TRACS: Dealing with a Deck of Double-Sided Cards. - Colin Fyfe:
Incrementally Learned Subjectivist Probabilities in Games. - Timo Steffens:
Similarity-based Opponent Modelling using Imperfect Domain Theories. - Haruhiro Yoshimoto, Rie Shigetomi, Hideki Imai:
How to Protect Peer-to-Peer Online Games from Cheats. - Gayle Leen, Colin Fyfe:
Training an AI Player to play Pong Using the GTM. - Jordan B. Pollack:
Nannon: A Nano Backgammon for Machine Learning Research.
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