On the complexity of blocks-world planning
Artificial Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Complexity, decidability and undecidability results for domain-independent planning
Artificial Intelligence - Special volume on planning and scheduling
Fast planning through planning graph analysis
Artificial Intelligence
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Local search topology in planning benchmarks: an empirical analysis
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Sensor networks and distributed CSP: communication, computation and complexity
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Search in the patience game ‘Black Hole’
AI Communications - Constraint Programming for Planning and Scheduling
Learning from planner performance
Artificial Intelligence
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Approximation Properties of Planning Benchmarks
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Of Mechanism Design Multiagent Planning
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Simultaneous heuristic search for conjunctive subgoals
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
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
Where "Ignoring delete lists" works: local search topology in planning benchmarks
Journal of Artificial Intelligence Research
Engineering benchmarks for planning: the domains used in the deterministic part of IPC-4
Journal of Artificial Intelligence Research
The complexity of planning problems with simple causal graphs
Journal of Artificial Intelligence Research
New Islands of tractability of cost-optimal planning
Journal of Artificial Intelligence Research
Theoretical Computer Science
Sensor networks and distributed CSP: communication, computation and complexity
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Computational Complexity of Cast Puzzles
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
A meta-CSP model for optimal planning
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Understanding planning tasks: domain complexity and heuristic decomposition
Understanding planning tasks: domain complexity and heuristic decomposition
A method of software structure designing based on ant colony planning
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A weighted CSP approach to cost-optimal planning
AI Communications
Implicit abstraction heuristics
Journal of Artificial Intelligence Research
GA-FreeCell: evolving solvers for the game of FreeCell
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Analyzing search topology without running any search: on the connection between causal graphs and h+
Journal of Artificial Intelligence Research
MFCS'07 Proceedings of the 32nd international conference on Mathematical Foundations of Computer Science
On the complexity of planning for agent teams and its implications for single agent planning
Artificial Intelligence
A refined view of causal graphs and component sizes: SP-closed graph classes and beyond
Journal of Artificial Intelligence Research
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The efficiency of AI planning systems is usually evaluated empirically. For the validity of conclusions drawn from such empirical data, the problem set used for evaluation is of critical importance. In planning, this problem set usually, or at least often, consists of tasks from the various planning domains used in the first two international planning competitions, hosted at the 1998 and 2000 AIPS conferences. It is thus surprising that comparatively little is known about the properties of these benchmark domains, with the exception of BLOCKSWORLD, which has been studied extensively by several research groups.In this contribution, we try to remedy this fact by providing a map of the computational complexity of non-optimal and optimal planning for the set of domains used in the competitions. We identify a common transportation theme shared by the majority of the benchmarks and use this observation to define and analyze a general transportation problem that generalizes planning in several classical domains such as LOGISTICS, MYSTERY and GRIPPER. We then apply the results of that analysis to the actual transportation domains from the competitions. We next examine the remaining benchmarks, which do not exhibit a strong transportation feature, namely SCHEDULE and FREECELL.Relating the results of our analysis to empirical work on the behavior of the recently very successful FF planning system, we observe that our theoretical results coincide well with data obtained from empirical investigations.