By Topic

Handling Uncertainty in Least Committed Graphplan: A Conformant Approach

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

4 Author(s)
Jing-Bo Zhang ; Dept. of Comput. Sci., Northeast Normal Univ., Jilin ; You-Hong Zhang ; Wen-Xiang Gu ; Jia-Nan Wang

An artificial intelligent planner called conformant least committed graphplan (CLCGP) is proposed in this paper. This planner can handle uncertainty even without sensory information, which means it is possible to find valid plans no matter which of the allowed states the world is actually in. CLCGP is based on the famous planner LCGP, which has been proved to have great success in solving classic planning domains. The basic idea of this algorithm is to develop separate least committed planning graph for each possible world. The planner is implemented in common Lisp and tested on a IBM RS6000 machine, empirical results show that CLCGP performs significantly better than the famous conformant planner CGP

Published in:

Machine Learning and Cybernetics, 2006 International Conference on

Date of Conference:

13-16 Aug. 2006