ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
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
Algorithm performance and problem structure for flow-shop scheduling
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Complexity results for standard benchmark domains in planning
Artificial Intelligence
Domain-Independent Online Planning for STRIPS Domains
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Ignoring Irrelevant Facts and Operators in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Exploiting Competitive Planner Performance
ECP '99 Proceedings of the 5th 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
Extending Planning Graphs to an ADL Subset
Extending Planning Graphs to an ADL Subset
Planning with Goal Agendas
Utilizing Problem Structure in Planning: A Local Search Approach
Utilizing Problem Structure in Planning: A Local Search Approach
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Branching and pruning: an optimal temporal POCL planner based on constraint programming
AAAI'04 Proceedings of the 19th national conference on Artifical 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
Sapa: a multi-objective metric temporal planner
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
VHPOP: versatile heuristic partial order planner
Journal of Artificial Intelligence Research
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
An approach to temporal planning and scheduling in domains with predictable exogenous events
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Temporal planning using subgoal partitioning and resolution in SGPlan
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
Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Long-distance mutual exclusion for propositional planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Local search topology in planning benchmarks: an empirical analysis
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
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
New methods for proving the impossibility to solve problems through reduction of problem spaces
Annals of Mathematics and Artificial Intelligence
Analyzing search topology without running any search: on the connection between causal graphs and h+
Journal of Artificial Intelligence Research
Automating the evaluation of planning systems
AI Communications
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The planning community has amassed a large body of publicly available problems in a standardized input language and planners that accept the language. We seized this remarkable opportunity to collect data about how some of these planners perform on the benchmark problems. We analyzed the resulting data to learn about the state of the art in Classical planning. Our analyses are retrospective, prescriptive and prospective. The first analyses are retrospective and prescriptive in that they characterize the problems and planners in terms of difficulty, diversity and trends over time. We statistically confirm that problem sets have become more difficult and that new planners are generally more capable. A visualization of planner success on domains shows how the domains distinguish performance. We also assess whether some older planners can be disregarded as out-dated and find that while they are not up to current capabilities, some do provide limited distinct functionality. The second analyses automatically learn models of success and time for each planner. The models are constructed from easily extracted features of problems and domains and use off-the-shelf Machine Learning techniques. We find the models of success to be extremely accurate, but the models of time to be less so. They too are both retrospective and prescriptive in demonstrating the predictability of current planner performance. In a third analysis, we apply the data to an existing explanatory model linking the relationship between the search space and planner performance. Our study validates previous results linking search topology with planner performance on a wider set of planners than the original study. Finally, we fill in some gaps in observed performance of the benchmark problems by constructing new problems; these problems do turn out to be more challenging. This study of existing and new problems and planners is prescriptive and prospective in that the results should help guide researchers in comparatively evaluating their planners and suggest need for additional effort. These analyses highlight the importance of problems in driving research in planning. We show how much can be accomplished with the available resources and point out how much more can be done by broadening the problems available and by learning from what has already been done.