Planning for conjunctive goals
Artificial Intelligence
Principles of artificial intelligence
Principles of 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
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
Accelerating Partial Order Planners by Improving Plan and Goal Choices
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
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
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Incremental Local Search for Planning Problems
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Exploring artificial intelligence in the new millennium
Using Automated Planning for Trusted Self-organising Organic Computing Systems
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
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
Using memory to transform search on the planning graph
Journal of Artificial Intelligence Research
Flaw selection strategies for partial-order planning
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
A critical assessment of benchmark comparison in planning
Journal of Artificial Intelligence Research
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A linear programming heuristic for optimal planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Multiagent argumentation for cooperative planning in DeLP-POP
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
An architecture for defeasible-reasoning-based cooperative distributed planning
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Hybrid planning using flexible strategies
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Dialectical theory for multi-agent assumption-based planning
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Cooperative dialogues for defeasible argumentation-based planning
ArgMAS'11 Proceedings of the 8th international conference on Argumentation in Multi-Agent Systems
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We propose some domain-independent techniques for bringing well-founded partial-order planners closer to practicality. The first two techniques are aimed at improving search control while keeping overhead costs low. One is based on a simple adjustment to the default A* heuristic used by ucpop to select plans for refinement. The other is based on preferring "zero commitment" (forced) plan refinements whenever possible, and using LIFO prioritization otherwise. A more radical technique is the use of operator parameter domains to prune search. These domains are initially computed from the definitions of the operators and the initial and goal conditions, using a polynomial-time algorithm that propagates sets of constants through the operator graph, starting in the initial conditions. During planning, parameter domains can be used to prune nonviable operator instances and to remove spurious clobbering threats. In experiments based on modifications of ucpop, our improved plan and goal selection strategies gave speedups by factors ranging from 5 to more than 1000 for a variety of problems that are nontrivial for the unmodified version. Crucially, the hardest problems gave the greatest improvements. The pruning technique based on parameter domains often gave speedups by an order of magnitude or more for difficult problems, both with the default ucpop search strategy and with our improved strategy. The Lisp code for our techniques and for the test problems is provided in on-line appendices.