A Heuristic for Hybrid Planning with Preferences

A Heuristic for Hybrid Planning with Preferences. Bercher, P. & Biundo, S. In Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2012), pages 120–123, 2012. AAAI Press.

Paper

Poster abstract bibtex

In this paper, we introduce an admissible heuristic for hybrid planning with preferences. Hybrid planning is the fusion of hierarchical task network (HTN) planning with partial order causal link (POCL) planning. We consider preferences to be soft goals – facts one would like to see satisfied in a goal state, but which do not have to hold necessarily. Our heuristic estimates the best quality of any solution that can be developed from the current plan under consideration. It can thus be used by any branch-and-bound algorithm that performs search in the space of plans to prune suboptimal plans from the search space.

@InProceedings{Bercher2012HTNPreferenceHeuristic,
  author     = {Pascal Bercher and Susanne Biundo},
  title      = {A Heuristic for Hybrid Planning with Preferences},
  booktitle  = {Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference ({FLAIRS} 2012)},
  year       = {2012},
  publisher  = {{AAAI} Press},
  pages      = {120--123},
  abstract   = {In this paper, we introduce an admissible heuristic for hybrid planning with preferences. Hybrid planning is the fusion of hierarchical task network (HTN) planning with partial order causal link (POCL) planning. We consider preferences to be soft goals -- facts one would like to see satisfied in a goal state, but which do not have to hold necessarily. Our heuristic estimates the best quality of any solution that can be developed from the current plan under consideration. It can thus be used by any branch-and-bound algorithm that performs search in the space of plans to prune suboptimal plans from the search space.},
  url_Paper  = {https://bercher.net/publications/2012/Bercher2012HTNPreferenceHeuristic.pdf},
  url_Poster = {https://bercher.net/publications/2012/Bercher2012HTNPreferenceHeuristicPoster.pdf},
  keywords   = {conference}
}

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