Principles of artificial intelligence
Principles of artificial intelligence
Efficient implementation of a BDD package
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Extending Graphplan to handle uncertainty and sensing actions
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
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Algorithms and Data Structures in VLSI Design
Algorithms and Data Structures in VLSI Design
Understanding and Extending Graphplan
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
Speeding up the calculation of heuristics for heuristic search-based planning
Eighteenth national conference on Artificial intelligence
Handling of Conditional Effects and Negative Goals in IPP
Handling of Conditional Effects and Negative Goals in IPP
The FF planning system: fast plan generation through heuristic search
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
Using memory to transform search on the planning graph
Journal of Artificial Intelligence Research
The GRT planning system: backward heuristic construction in forward state-space planning
Journal of Artificial Intelligence Research
Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Planning in nondeterministic domains under partial observability via symbolic model checking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Conditional planning in the discrete belief space
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Finding state similarities for faster planning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Artificial Intelligence
A conformant planner based on approximation: CpA(H)
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states. The naive technique enumerates the graphs individually. This is equivalent to solving an all-pairs shortest path problem by iterating a single-source algorithm over each source. We introduce a structure, the state agnostic planning graph, that directly, solves the all-pairs problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in classical planning. The more prominent application of tnis technique is in belief-space planning, where an optimization results in drastically improved theoretical complexity. Our experimental evaluation quantifies this performance boost. and demonstrates that heuristic belief-space progression planning using our technique is competitive with the state of t the art.