A CBR Inspired Approach to Rapid and Reliable Adaption of Video Game Al

Current approaches to adaptive game AI typically require numerous trials to learn effective behaviour (i.e., game adaptation is not rapid). In addition, game developers are concerned that applying adaptive game AI may result in uncontrollable and unpredictable behaviour (i.e., game adaptation is not reliable). These characteristics hamper the incorporation of adaptive game AI in commercially available video games. In this article, we discuss an alternative to these approaches. In the case-based inspired approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource intensive learning) to evoke effective behaviour in a controlled manner in online play. We performed experiments that test case-based adaptive game AI on three different maps in a commercial RTS game. From our results we may conclude that case-based adaptive game AI provides a strong basis for effectively adapting game AI in video games.

[1]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[2]  Marc J. V. Ponsen,et al.  Improving Adaptive Game Ai with Evolutionary Learning , 2004 .

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

[4]  Sushil J. Louis,et al.  Playing to learn: case-injected genetic algorithms for learning to play computer games , 2005, IEEE Transactions on Evolutionary Computation.

[5]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

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

[7]  H. Jaap van den Herik,et al.  Rapid and Reliable Adaptation of Video Game AI , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[8]  A. Ram,et al.  Authoring Behaviors for Games using Learning from Demonstration , 2009 .

[9]  Padraig Cunningham,et al.  Case-Based Plan Recognition in Computer Games , 2003, ICCBR.

[10]  Pieter Spronck,et al.  Automatically Generating a Score Function for Strategy Games , 2008 .

[11]  Santiago Ontañón,et al.  Case-Based Planning and Execution for Real-Time Strategy Games , 2007, ICCBR.

[12]  Ashwin Ram,et al.  Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL , 2007, IJCAI.

[13]  Simon Colton,et al.  Combining AI Methods for Learning Bots in a Real-Time Strategy Game , 2009, Int. J. Comput. Games Technol..

[14]  H. Jaap van den Herik,et al.  Opponent modelling for case-based adaptive game AI , 2009, Entertain. Comput..

[15]  Paul R. Cohen,et al.  Empirical methods for artificial intelligence , 1995, IEEE Expert.

[16]  Shachar Lovett,et al.  Preface , 2012, COLT.