Planning for conjunctive goals
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
O-Plan: the open planning architecture
Artificial Intelligence
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
Efficient memory-bounded search methods
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Least-cost flaw repair: a plan refinement strategy for partial-order planning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Artificial Intelligence - Special volume on planning and scheduling
Passive and active decision postponement in plan generation
Passive and active decision postponement in plan generation
Accelerating partial-order planners: some techniques for effective search control and pruning
Journal of Artificial Intelligence Research
Comparison of methods for improving search efficiency in a partial-order planner
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Is "early commitment" in plan generation ever a good idea?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Commitment strategies in hierarchical task network planning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Analyzing external conditions to improve the efficiency of HTN planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
IPSS: A Hybrid Approach to Planning and Scheduling Integration
IEEE Transactions on Knowledge and Data Engineering
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
VHPOP: versatile heuristic partial order planner
Journal of Artificial Intelligence Research
Engineering a conformant probabilistic planner
Journal of Artificial Intelligence Research
A critical assessment of benchmark comparison in planning
Journal of Artificial Intelligence Research
Efficient and distributable methods for solving the multiagent plan coordination problem
Multiagent and Grid Systems - Planning in multiagent systems
Planning in domains with derived predicates through rule-action graphs and local search
Annals of Mathematics and Artificial Intelligence
An Empirical Analysis of Some Heuristic Features for Planning through Local Search and Action Graphs
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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Several recent studies have compared the relative efficiency of alternative flaw selection strategies for partial-order causal link (POCL) planning. We review this literature, and present new experimental results that generalize the earlier work and explain some of the discrepancies in it. In particular, we describe the Least-Cost Flaw Repair (LCFR) strategy developed and analyzed by Joslin and Pollack (1994), and compare it with other strategies, including Gerevini and Schubert's (1996) ZLIFO strategy. LCFR and ZLIFO make very different, and apparently conflicting claims about the most effective way to reduce search-space size in POCL planning. We resolve this conflict, arguing that much of the benefit that Gerevini and Schubert ascribe to the LIFO component of their ZLIFO strategy is better attributed to other causes. We show that for many problems, a strategy that combines least-cost flaw selection with the delay of separable threats will be effective in reducing search-space size, and will do so without excessive computational overhead. Although such a strategy thus provides a good default, we also show that certain domain characteristics may reduce its effectiveness.