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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