Getting the most out of pattern databases for classical planning

Authors:
Florian Pommerening;Gabriele Röger;Malte Helmert
Affiliations:
Universität Basel, Basel, Switzerland;Universität Basel, Basel, Switzerland;Universität Basel, Basel, Switzerland
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

The iPDB procedure by Haslum et al. is the state-of-the-art method for computing additive abstraction heuristics for domain-independent planning. It performs a hill-climbing search in the space of pattern collections, combining information from multiple patterns in the so-called canonical heuristic. We show how stronger heuristic estimates can be obtained through linear programming. An experimental evaluation demonstrates the strength of the new technique on the IPC benchmark suite.