The computational complexity of propositional STRIPS planning
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Using regression-match graphs to control search in planning
Artificial Intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Complexity results for standard benchmark domains in planning
Artificial Intelligence
Discovering State Constraints in DISCOPLAN: Some New Results
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Extracting Effective and Admissible State Space Heuristics from the Planning Graph
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Iterative Algorithm for Synthesizing Invariants
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Ignoring Irrelevant Facts and Operators in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Combining the Expressivity of UCPOP with the Efficiency of Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Utilizing Problem Structure in Planning: A Local Search Approach
Utilizing Problem Structure in Planning: A Local Search Approach
Artificial Intelligence
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
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
Taming numbers and durations in the model checking integrated planning system
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
The deterministic part of IPC-4: an overview
Journal of Artificial Intelligence Research
Engineering benchmarks for planning: the domains used in the deterministic part of IPC-4
Journal of Artificial Intelligence Research
When gravity fails: local search topology
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
The GRT planning system: backward heuristic construction in forward state-space planning
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Local search topology in planning benchmarks: an empirical analysis
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
SPIN'03 Proceedings of the 10th international conference on Model checking software
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
Concise finite-domain representations for PDDL planning tasks
Artificial Intelligence
Learning from planner performance
Artificial Intelligence
Accuracy of admissible heuristic functions in selected planning domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
The deterministic part of IPC-4: an overview
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
Engineering benchmarks for planning: the domains used in the deterministic part of IPC-4
Journal of Artificial Intelligence Research
Marvin: a heuristic search planner with online macro-action learning
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Set-structured and cost-sharing heuristics for classical planning
Annals of Mathematics and Artificial Intelligence
Friends or foes? on planning as satisfiability and abstract CNF encodings
Journal of Artificial Intelligence Research
An LP-based heuristic for optimal planning
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Understanding planning tasks: domain complexity and heuristic decomposition
Understanding planning tasks: domain complexity and heuristic decomposition
Planning with h+in theory and practice
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Brothers in Arms? On AI Planning and Cellular Automata
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Strengthening Landmark Heuristics via Hitting Sets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A weighted CSP approach to cost-optimal planning
AI Communications
Everything you always wanted to know about planning (but were afraid to ask)
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Analyzing search topology without running any search: on the connection between causal graphs and h+
Journal of Artificial Intelligence Research
Content determination through planning for flexible game tutorials
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Stochastic enforced hill-climbing
Journal of Artificial Intelligence Research
Avoiding and escaping depressions in real-time heuristic search
Journal of Artificial Intelligence Research
A case-based approach to heuristic planning
Applied Intelligence
The complexity of optimal monotonic planning: the bad, the good, and the causal graph
Journal of Artificial Intelligence Research
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Between 1998 and 2004, the planning community has seen vast progress in terms of the sizes of benchmark examples that domain-independent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The unprecedented success of such methods, in many commonly used benchmark examples, calls for an understanding of what classes of domains these methods are well suited for. In the investigation at hand, we derive a formal background to such an understanding. We perform a case study covering a range of 30 commonly used STRIPS and ADL benchmark domains, including all examples used in the first four international planning competitions. We prove connections between domain structure and local search topology - heuristic cost surface properties - under an idealized version of the heuristic functions used in modern planners. The idealized heuristic function is called h+, and differs from the practically used functions in that it returns the length of an optimal relaxed plan, which is NP-hard to compute. We identify several key characteristics of the topology under h+, concerning the existence/non-existence of unrecognized dead ends, as well as the existence/non-existence of constant upper bounds on the difficulty of escaping local minima and benches. These distinctions divide the (set of all) planning domains into a taxonomy of classes of varying h+ topology. As it turns out, many of the 30 investigated domains lie in classes with a relatively easy topology. Most particularly, 12 of the domains lie in classes where FF's search algorithm, provided with h+, is a polynomial solving mechanism. We also present results relating h+ to its approximation as implemented in FF. The behavior regarding dead ends is provably the same. We summarize the results of an empirical investigation showing that, in many domains, the topological qualities of h+ are largely inherited by the approximation. The overall investigation gives a rare example of a successful analysis of the connections between typical-case problem structure, and search performance. The theoretical investigation also gives hints on how the topological phenomena might be automatically recognizable by domain analysis techniques. We outline some preliminary steps we made into that direction.