Artificial Intelligence
Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Principles of artificial intelligence
Principles of artificial intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
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
Scalable planning under uncertainty
Scalable planning under uncertainty
Sequential Monte Carlo in reachability heuristics for probabilistic planning
Artificial Intelligence
PADL '09 Proceedings of the 11th International Symposium on Practical Aspects of Declarative Languages
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
State agnostic planning graphs and the application to belief-space planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Prottle: a probabilistic temporal planner
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Probabilistic planning via determinization in hindsight
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
The 3rd international planning competition: results and analysis
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
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
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
Universal plans for reactive robots in unpredictable environments
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
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
A translation-based approach to contingent planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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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, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source. We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source 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 deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i.e., search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.