Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Fast planning through planning graph analysis
Artificial Intelligence
Inferring state constraints for domain-independent planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using regression-match graphs to control search in planning
Artificial Intelligence
Ignoring Irrelevant Facts and Operators in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
GRT: A Domain Independent Heuristic for STRIPS Worlds Based on Greedy Regression Tables
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
On Determining and Completeing Incomplete States in STRIPS Domains
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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This paper presents recent extensions to the GRT planner, a domain-independent heuristic state-space planner for STRIPS worlds. The planner computes off-line, in a pre-processing phase, estimates for the distances between each problem's fact and the goals. These estimates are utilized during a forward search phase, in order to obtain values for the distances between the intermediate states and the goals.The paper focuses on several problems that arise from the backward heuristic computation and presents ways to cope with them. Moreover, two methods, which concern automatic domain enrichment and automatic irrelevant objects elimination, are presented. Finally, the planner has been equipped with a hillclimbing strategy and a closed list of visited states for pruning purposes. Performance results show that GRT exhibits significant improvement over its AIPS-00 competition version.