McClarron, P and Ollington, R and Lewis, I, Effect of constraints on evolving behavior trees for game AI, Proceedings of the 9th Annual International Conference on Computer Games Multimedia & Allied Technologies (CGAT 2016), 28-29 March 2016, Singapore, pp. 1-6. ISSN 2251-1679 (2016) [Refereed Conference Paper]
Official URL: http://www.cgames.com.sg/index.html
Behavior trees are a popular method for creating AI characters in games. They allow modular and hierarchical behaviors, making it easy to maintain, extend and modify behaviors for differ situations. Nevertheless, a considerable amount of skill, experience and time is required to produce believable behaviors.
Previous attempts to automate the development of behavior trees using genetic programming have met with limited success. One of the reasons for this is that random crossover and mutation of a behavior tree can result in large trees with many nonsensical branches. We investigate different methods for constraining crossover and mutation of the behavior tree in order to reduce the size of the search space and improve the resultant AI.
Preliminary experiments have focused on the game Pacman and we present results showing that constraining crossover and mutation so that the resultant behavior trees always maintain a sensible structure produces significantly better results than an unconstrained algorithm.
|Item Type:||Refereed Conference Paper|
|Keywords:||genetic programming, behaviour trees, game AI|
|Research Division:||Information and Computing Sciences|
|Research Group:||Machine learning|
|Research Field:||Neural networks|
|Objective Division:||Information and Communication Services|
|Objective Group:||Other information and communication services|
|Objective Field:||Other information and communication services not elsewhere classified|
|UTAS Author:||McClarron, P (Mr Paul McClarron)|
|UTAS Author:||Ollington, R (Dr Robert Ollington)|
|UTAS Author:||Lewis, I (Dr Ian Lewis)|
|Deposited By:||Information and Communication Technology|
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