By Topic

Neurovisual Control in the Quake II Environment

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)
Parker, M. ; Dept. of Comput. Sci. & Eng., Univ. of Nevada, Reno, NV, USA ; Bryant, B.D.

A wide variety of tasks may be performed by humans using only visual data as input. Creating artificial intelligence that adequately uses visual data allows controllers to use single cameras for input and to interact with computer games by merely reading the screen render. In this research, we use the Quake II game environment to compare various techniques that train neural network (NN) controllers to perform a variety of behaviors using only raw visual input. First, it is found that a humanlike retina, which has greater acuity in the center and less in the periphery, is more useful than a uniform acuity retina, both having the same number of inputs and interfaced to the same NN structure, when learning to attack a moving opponent in a visually simple room. Next, we use the same humanlike retina and NN in a more visually complex room, but, finding it is unable to learn successfully, we use a Lamarckian learning algorithm with a nonvisual hand-coded controller as a supervisor to help train the visual controller via backpropagation. Last, we replace the hand-coded supervising nonvisual controller with an evolved nonvisual NN controller, eliminating the human aspect from the supervision, and it solves a problem for which a solution was not previously known.

Published in:

Computational Intelligence and AI in Games, IEEE Transactions on  (Volume:4 ,  Issue: 1 )