Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
The computational complexity of propositional STRIPS planning
Artificial Intelligence
State-variable planning under structural restrictions: algorithms and complexity
Artificial Intelligence
Model checking
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Machine Discovery of Effective Admissible Heuristics
Machine Learning
Complexity results for standard benchmark domains in planning
Artificial Intelligence
New admissible heuristics for domain-independent planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Domain-independent construction of pattern database heuristics for cost-optimal planning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Accuracy of admissible heuristic functions in selected planning domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Additive pattern database heuristics
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
A general theory of additive state space abstractions
Journal of Artificial Intelligence Research
The role of macros in tractable planning over causal graphs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Optimal symbolic planning with action costs and preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Cost-optimal planning with landmarks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Friends or foes? on planning as satisfiability and abstract CNF encodings
Journal of Artificial Intelligence Research
Understanding planning tasks: domain complexity and heuristic decomposition
Understanding planning tasks: domain complexity and heuristic decomposition
Optimal admissible composition of abstraction heuristics
Artificial Intelligence
Directed model checking with distance-preserving abstractions
SPIN'06 Proceedings of the 13th international conference on Model Checking Software
Landmark-enhanced abstraction heuristics
Artificial Intelligence
Implicit abstraction heuristics for cost-optimal planning
AI Communications
Online speedup learning for optimal planning
Journal of Artificial Intelligence Research
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
The complexity of optimal monotonic planning: the bad, the good, and the causal graph
Journal of Artificial Intelligence Research
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State-space search with explicit abstraction heuristics is at the state of the art of cost-optimal planning. These heuristics are inherently limited, nonetheless, because the size of the abstract space must be bounded by some, even if a very large, constant. Targeting this shortcoming, we introduce the notion of (additive) implicit abstractions, in which the planning task is abstracted by instances of tractable fragments of optimal planning. We then introduce a concrete setting of this framework, called fork-decomposition, that is based on two novel fragments of tractable cost-optimal planning. The induced admissible heuristics are then studied formally and empirically. This study testifies for the accuracy of the fork decomposition heuristics, yet our empirical evaluation also stresses the tradeoff between their accuracy and the runtime complexity of computing them. Indeed, some of the power of the explicit abstraction heuristics comes from precomputing the heuristic function offine and then determining h(s) for each evaluated state s by a very fast lookup in a "database." By contrast, while fork-decomposition heuristics can be calculated in polynomial time, computing them is far from being fast. To address this problem, we show that the time-per-node complexity bottleneck of the fork-decomposition heuristics can be successfully overcome. We demonstrate that an equivalent of the explicit abstraction notion of a "database" exists for the fork-decomposition abstractions as well, despite their exponential-size abstract spaces. We then verify empirically that heuristic search with the "databased" fork-decomposition heuristics favorably competes with the state of the art of cost-optimal planning.