By Topic

Distributed case-based learning

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

1 Author(s)
Nagendra Prasad, M.V. ; Adv. Products & Res. Group, VerticalNet, Palo Alto, CA, USA

Multi-agent systems exploiting case-based reasoning techniques have to deal with the problem of retrieving episodes that are themselves distributed across a set of agents. From a Gestalt perspective, a good overall case may not be the one derived from the summation of best sub-cases. We deal with issues involved in learning and exploiting the learned knowledge in multi-agent case-based systems. We propose a novel algorithm called OA*, which composes optimal overall cases from distributed case components, and prove its optimality. We then experiment with OA* in a transportation domain on a grid world. We provide empirical results that provide strong evidence of the effectiveness of OA* for the distributed case-based learning task

Published in:

MultiAgent Systems, 2000. Proceedings. Fourth International Conference on

Date of Conference: