SCOUT: A Case-Based Reasoning Agent for Playing Race for the Galaxy

Game AI is a well-established area of research. Classic strategy board games such as Chess and Go have been the subject of AI research for several decades, and more recently modern computer games have come to be seen as a valuable test-bed for AI methods and technologies. Modern board games, in particular those known as German-Style Board Games or Eurogames, are an interesting mid-point between these fields in terms of domain complexity, but AI research in this area is more sparse. This paper discusses the design, development and performance of a game-playing agent, called SCOUT, that uses the Case-Based Reasoning methodology as a means to reason and make decisions about game states in the Eurogame Race for the Galaxy. The purpose of this research is to explore the possibilities and limitations of Case-Based Reasoning within the domain of Race for the Galaxy and Eurogames in general.

[1]  John E. Laird,et al.  Human-Level AI's Killer Application: Interactive Computer Games , 2000, AI Mag..

[2]  Peter I. Cowling,et al.  Monte Carlo search applied to card selection in Magic: The Gathering , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[3]  Ian D. Watson,et al.  Investigating the Effectiveness of Applying Case-Based Reasoning to the Game of Texas Hold'em , 2007, FLAIRS.

[4]  Jonathan Schaeffer,et al.  Checkers Is Solved , 2007, Science.

[5]  Barry Smyth,et al.  Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems , 1995, IJCAI.

[6]  Christopher K. Riesbeck,et al.  Inside Case-Based Reasoning , 1989 .

[7]  Carlos Morell,et al.  KNN behavior with set-valued attributes , 2010, ESANN.

[8]  A. Arbor,et al.  Case-Based Learning Algorithms , 1991 .

[9]  Lili Sahakyan,et al.  Remembering to Forget , 2010, Psychological science.

[10]  Berseker Race for the Galaxy , 2010 .

[11]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[12]  Claire Cardie,et al.  Improving Minority Class Prediction Using Case-Specific Feature Weights , 1997, ICML.

[13]  Pieter Spronck,et al.  Monte-Carlo Tree Search in Settlers of Catan , 2009, ACG.

[14]  Sebastián Ventura,et al.  Evolutionary feature weighting to improve the performance of multi-label lazy algorithms , 2014, Integr. Comput. Aided Eng..

[15]  Jonathan Rubin,et al.  CASPER: DESIGN AND DEVELOPMENT OF A CASE-BASED POKER PLAYER , 2007 .

[16]  David W. Aha,et al.  Learning to Win: Case-Based Plan Selection in a Real-Time Strategy Game , 2005, Künstliche Intell..

[17]  Cathleen Heyden,et al.  IMPLEMENTING A COMPUTER PLAYER FOR CARCASSONNE , 2009 .

[18]  Jay H. Powell,et al.  Utilizing Case-Based Reasoning and Automatic Case Elicitation to Develop a Self-Taught Knowledgeable Agent , 2004 .

[19]  Ian D. Watson,et al.  Combining Case-Based Reasoning and Reinforcement Learning for Unit Navigation in Real-Time Strategy Game AI , 2014, ICCBR.

[20]  Hector Muñoz-Avila,et al.  Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning , 2008, ECCBR.

[21]  Murray Campbell,et al.  Deep Blue , 2002, Artif. Intell..

[22]  Michael Buro,et al.  Call for AI Research in RTS Games , 2004 .

[23]  David W. Aha,et al.  A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.

[24]  David C. Wilson,et al.  Remembering Why to Remember: Performance-Guided Case-Base Maintenance , 2000, EWCBR.

[25]  Marc Parizeau,et al.  DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..

[26]  Neil Burch,et al.  Heads-up limit hold’em poker is solved , 2015, Science.

[27]  Michael M. Richter,et al.  Case-Based Reasoning , 2013, Springer Berlin Heidelberg.

[28]  David Sinclair,et al.  Using Example-Based Reasoning for Selective Move Generation in Two Player Adversarial Games , 1998, EWCBR.

[29]  Samuele Pedroni,et al.  PyPy's approach to virtual machine construction , 2006, OOPSLA '06.

[30]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[31]  Ian D. Watson,et al.  Case-based reasoning is a methodology not a technology , 1999, Knowl. Based Syst..

[32]  David B. Fogel,et al.  Blondie24: Playing at the Edge of AI , 2001 .

[33]  David W. Aha,et al.  TIELT: A Testbed for Gaming Environments , 2005, AAAI.

[34]  Richard S. Sutton,et al.  Temporal credit assignment in reinforcement learning , 1984 .

[35]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..