Counterexample-Guided Cartesian Abstraction Refinement for Classical Planning

@article{Seipp2018CounterexampleGuidedCA,
  title={Counterexample-Guided Cartesian Abstraction Refinement for Classical Planning},
  author={Jendrik Seipp and Malte Helmert},
  journal={J. Artif. Intell. Res.},
  year={2018},
  volume={62},
  pages={535-577}
}
Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and refine the abstraction so that the same failure cannot occur in future iterations. A key… 

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References

SHOWING 1-10 OF 46 REFERENCES

Counterexample-guided Abstraction Refinement for Classical Planning Master's Thesis Erklärung (declaration) Zusammenfassung

TLDR
This work proposes a CEGAR algorithm for computing abstraction heuristics for optimal classical planning and shows how refining the abstraction online when the search errs reduces the number of expanded nodes.

Merge-and-shrink abstractions for classical planning : theory, strategies, and implementation

TLDR
This thesis provides a comprehensive description of the merge-and-shrink framework in terms of transformations of transition systems, and describes an optimized implementation of the merged heuristics framework that substantially improves the efficiency compared to previous implementations.

Flexible Abstraction Heuristics for Optimal Sequential Planning

TLDR
An approach to deriving consistent heuristics for automated planning, based on explicit search in abstract state spaces, which subsumes planning with pattern databases as a special case and shows that the approach is competitive with the state of the art.

Relaxation Refinement: A New Method to Generate Heuristic Functions

TLDR
This work pioneer the application of (predicate) abstraction refinement for the generation of heuristic functions that are intelligently adapted to the problem at hand and investigates how an abstraction refinement process for generating heuristic function should differ from the process used in the verification context.

Counterexample-guided Planning

TLDR
This work extends the counterexample-guided abstraction refinement paradigm to probabilistic models (namely, perfect information games and, as a special case, MDPs), which allows it to apply to the AI planning problem.

Optimal admissible composition of abstraction heuristics

The Fast Downward Planning System

  • M. Helmert
  • Computer Science
    J. Artif. Intell. Res.
  • 2006
TLDR
A full account of Fast Downward's approach to solving multivalued planning tasks is given and a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way is presented.

Narrowing the Gap Between Saturated and Optimal Cost Partitioning for Classical Planning

TLDR
It is shown that searching in the space of orders leading to significantly better heuristic estimates than with previously considered orders and using multiple orders leads to a heuristic that is significantly better informed than any single-order heuristic.

Concise finite-domain representations for PDDL planning tasks

Merge-and-Shrink Abstraction

Many areas of computer science require answering questions about reachability in compactly described discrete transition systems. Answering such questions effectively requires techniques to be able