Boosting Search Guidance in Problems with Semantic Attachments

Authors

  • Sara Bernardini Royal Holloway, University of London
  • Maria Fox King's College London
  • Derek Long King's College London
  • Chiara Piacentini University of Toronto

DOI:

https://doi.org/10.1609/icaps.v27i1.13807

Abstract

Most applications of planning to real problems involve complex and often non-linear equations, including matrix operations. PDDL is ill-suited to express such calculations since it only allows basic operations between numeric fluents. To remedy this restriction, a generic PDDL planner can be connected to a specialised advisor, which equips the planner with the ability to carry out sophisticated mathematical operations. Unlike related techniques based on semantic attachment, our planner is able to exploit an approximation of the numeric information calculated by the advisor to compute informative heuristic estimators. Guided by both causal and numeric information, our planning framework outperforms traditional approaches, especially against problems with numeric goals. We provide evidence of the power of our solution by successfully solving four completely different problems.

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Published

2017-06-05

How to Cite

Bernardini, S., Fox, M., Long, D., & Piacentini, C. (2017). Boosting Search Guidance in Problems with Semantic Attachments. Proceedings of the International Conference on Automated Planning and Scheduling, 27(1), 29-37. https://doi.org/10.1609/icaps.v27i1.13807