Applying Learning by Observation and Case-Based Reasoning to Improve Commercial RTS Game AI

This document summarises my research in the area of RealTime Strategy (RTS) video game Artificial Intelligence (AI). The main objective of this research is to increase the quality of AI used in commercial RTS games, which has seen little improvement over the past decade. This objective will be addressed by investigating the use of a learning by observation, case-based reasoning agent, which can be applied to new RTS games with minimal development effort. To be successful, this agent must compare favourably with standard commercial RTS AI techniques: it must be easier to apply, have reasonable resource requirements, and produce a better player. Currently, a prototype implementation has been produced for the game StarCraft, and it has demonstrated the need for processing large sets of input data into a more concise form for use at run-time.

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

[2]  D. Aha,et al.  Case-Based Learning in Goal-Driven Autonomy Agents for Real-Time Strategy Combat Tasks , 2011 .

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

[4]  D. McDowell.  Foreword , 1999 .

[5]  Michael Buro,et al.  RTS Games and Real-Time AI Research , 2003 .

[6]  Santiago Ontañón,et al.  Learning from Demonstration and Case-Based Planning for Real-Time Strategy Games , 2008, Soft Computing Applications in Industry.

[7]  Santiago Ontañón,et al.  Using Automated Replay Annotation for Case-Based Planning in Games , 2010 .

[8]  Arnav Jhala,et al.  Applying Goal-Driven Autonomy to StarCraft , 2010, AIIDE.

[9]  Mark Collins,et al.  AI Techniques for Game Programming , 2002 .

[10]  Pedro Pablo Gómez-Martín,et al.  Query-Enabled Behavior Trees , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

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

[12]  Babak Esfandiari,et al.  A Case-Based Reasoning Framework for Developing Agents Using Learning by Observation , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[13]  Arnav Jhala,et al.  Learning from Demonstration for Goal-Driven Autonomy , 2012, AAAI.

[14]  Michael W. Floyd,et al.  Comparison of Classifiers for use in a Learning by Demonstration System for a Situated Agent , 2009 .

[15]  Ian Lane Davis,et al.  Strategies for Strategy Game AI , 1999 .