Case-Based Reasoning and User-Generated Artificial Intelligence for Real-Time Strategy Games

Creating artificial intelligence (AI) for complex computer games requires a great deal of technical knowledge as well as engineering effort on the part of game developers. This chapter focuses on techniques that enable end-users to create AI for games without requiring technical knowledge using case-based reasoning (CBR) techniques. AI creation for computer games typically involves two steps: (a) generating a first version of the AI, and (b) debugging and adapting it via experimentation. We will use the domain of real-time strategy games to illustrate how CBR can address both steps.

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

[2]  Santiago Ontañón,et al.  An Intelligent IDE for Behavior Authoring in Real-Time Strategy Games , 2008, AIIDE.

[3]  Eric O. Postma,et al.  Adaptive game AI with dynamic scripting , 2006, Machine Learning.

[4]  Babak Esfandiari,et al.  A Case-Based Reasoning Approach to Imitating RoboCup Players , 2008, FLAIRS.

[5]  Santiago Ontañón,et al.  Using Meta-reasoning to Improve the Performance of Case-Based Planning , 2009, ICCBR.

[6]  David W. Aha,et al.  Lazy Learning , 1997, Springer Netherlands.

[7]  Santiago Onta,et al.  Learning from Human Demonstrations for Real-Time Case-Based Planning , 2009 .

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

[9]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[10]  Luca Spalzzi,et al.  A Survey on Case-Based Planning , 2001 .

[11]  Santiago Ontañón,et al.  On-Line Case-Based Plan Adaptation for Real-Time Strategy Games , 2008, AAAI.

[12]  Jun Morimoto,et al.  Learning from demonstration and adaptation of biped locomotion , 2004, Robotics Auton. Syst..

[13]  Santiago Ontañón,et al.  ON‐LINE CASE‐BASED PLANNING , 2010, Comput. Intell..

[14]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[15]  Michael Anthony Bauer,et al.  Programming by Examples , 1986, Artif. Intell..

[16]  Agnar Aamodt,et al.  CASE-BASED REASONING: FOUNDATIONAL ISSUES, METHODOLOGICAL VARIATIONS, AND SYSTEM APPROACHES AICOM - ARTIFICIAL INTELLIGENCE COMMUNICATIONS , 1994 .

[17]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[18]  Ashwin Ram,et al.  Introspective Multistrategy Learning: On the Construction of Learning Strategies , 1999, Artif. Intell..

[19]  Marco Antonio Gómez-Martín,et al.  Dynamic Expansion of Behaviour Trees , 2008, AIIDE.

[20]  Peng Zang,et al.  Towards Runtime Behavior Adaptation for Embodied Characters , 2007, IJCAI.

[21]  T. Lozano-Perez,et al.  Robot programming , 1983, Proceedings of the IEEE.

[22]  Maja J. Matarić,et al.  A framework for learning from demonstration, generalization and practice in human-robot domains , 2003 .

[23]  Henry Lieberman,et al.  Tinker: a programming by demonstration system for beginning programmers , 1993 .