Title :
Handling Uncertainty in Least Committed Graphplan: A Conformant Approach
Author :
Zhang, Jing-bo ; Zhang, You-hong ; Gu, Wen-xiang ; Wang, Jia-nan
Author_Institution :
Dept. of Comput. Sci., Northeast Normal Univ., Jilin
Abstract :
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
Keywords :
graph theory; planning (artificial intelligence); uncertainty handling; IBM RS6000 machine; Lisp; artificial intelligent planner; conformant least committed graphplan; uncertainty handling; Artificial intelligence; Authorization; Computer science; Cybernetics; Educational institutions; Intelligent sensors; Machine intelligence; Machine learning; Performance evaluation; Process planning; Testing; Uncertainty; Graphplan; Intelligent planning; Least-Commitment; conformant planning;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258880
