By Topic

Evolving Multimodal Networks for Multitask Games

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Schrum, J. ; Dept. of Comput. Sci., Univ. of Texas at Austin, Austin, TX, USA ; Miikkulainen, R.

Intelligent opponent behavior makes video games interesting to human players. Evolutionary computation can discover such behavior, however, it is challenging to evolve behavior that consists of multiple separate tasks. This paper evaluates three ways of meeting this challenge via neuroevolution: 1) multinetwork learns separate controllers for each task, which are then combined manually; 2) multitask evolves separate output units for each task, but shares information within the network's hidden layer; and 3) mode mutation evolves new output modes, and includes a way to arbitrate between them. Whereas the fist two methods require that the task division be known, mode mutation does not. Results in Front/Back Ramming and Predator/Prey games show that each of these methods has different strengths. Multinetwork is good in both domains, taking advantage of the clear division between tasks. Multitask performs well in Front/Back Ramming, in which the relative difficulty of the tasks is even, but poorly in Predator/Prey, in which it is lopsided. Interestingly, mode mutation adapts to this asymmetry and performs well in Predator/Prey. This result demonstrates how a human-specified task division is not always the best. Altogether the results suggest how human knowledge and learning can be combined most effectively to evolve multimodal behavior.

Published in:

Computational Intelligence and AI in Games, IEEE Transactions on  (Volume:4 ,  Issue: 2 )