By Topic

Evolutionary online learning of cooperative behavior with situation-action pairs

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)
Denzinger, J. ; Fachbereich Inf., Kaiserslautern Univ., Germany ; Kordt, M.

We present a concept to use off-line learning approaches to achieve online learning of cooperative behavior of agents and instantiate this concept for evolutionary learning with agents based on prototype situation-action-pairs and the nearest-neighbor rule. For such an agent model also modeling of other agents can be achieved using the agent's own architecture with situation-action-pairs derived from observations. We tested our online learning agents for different variants of the pursuit game and characterize the aspects of variants for which our online learning agents outperform off-line learning ones. Since our concept also allows a smooth transition from off-line learning to online learning and vice versa, the resulting system is able to win much more game variants than systems using either on- or off-line learning exclusively

Published in:

MultiAgent Systems, 2000. Proceedings. Fourth International Conference on

Date of Conference:

2000