Planning for conjunctive goals
Artificial Intelligence
Planning as search: a quantitative approach
Artificial Intelligence
ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Commitment strategies in planning: a comparative analysis
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Admissible pruning strategies based on plan minimality for plan-space planning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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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In this paper, we present VVPLAN, a planner based on a classical state space search algorithm. The language used for domain and problem representation is ADL (Pednault 1989). We have compared VVPLAN to UCPOP (Penberthy and Weld 1992)(Weld 1994), a planner that admits the same representation language. Our experiments prove that such an algorithm is often more efficient than a planner based on a search in the space of partial plans. This result is achieved as soon as we introduce in VVPLAN's algorithm a loop test relating to previously visited states. In particular domains, VVPLAN can also outperform IPP (Koehler et al. 1997), which makes a planning graph analysis as GRAPHPLAN. We present here the details of our comparison with UCPOP, the results we obtain and our conclusions.