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Discovering state constraints for planning with conditional effects in Discoplan (part I)

  • Alfonso Emilio GereviniEmail author
  • Lenhart Schubert
Article
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Abstract

Discoplan is a durable and efficient system for inferring state constraints (invariants) in planning domains, specified in the PDDL language. It is exceptional in the range of constraint types it can discover and verify, and it directly allows for conditional effects in action operators. However, although various aspects of Discoplan have been previously described and its utility in planning demonstrated, the underlying methodology, the algorithms for the discovery and inductive verification of constraints, and the proofs of correctness of the algorithms and their complexity analysis have never been laid out in adequate detail. The purpose of this paper is to remedy these lacunae.

Keywords

Automated planning Inference of state constraints for planning Planning domain analysis State invariants in planning Planning with conditional effects Knowledge discovery for planning Automatic inductive proofs 

Mathematics Subject Classification (2010)

68T01 68T27 68T35 68T30 68T20 

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Notes

Acknowledgments

This research was supported in part by NATO Collaborative Research Grant CRG951285, and by ARPA/SSTO grant F30602-95-1-0025 and DARPA grants F30602-98-2-0133 and W911NF-15-1-0542 (second author). We would like to thank Fabrizio Morbini for his valuable help in the construction of the on-line version of Discoplan, as well as in the implementation of a few parts of the system.

This work originated in the context of a collaboration between the Dept. of Computer Science of the University of Rochester and IRST in Trento, where the first author worked in a research group lead by Oliviero Stock who guided the start of his career in AI. We deeply thank Oliviero who initiated and supported this collaboration, initially focused on temporal reasoning and then extended to AI planning. This led to a very fruitful joint work between the authors of this paper, that has continued for several years.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversitá degli Studi di BresciaBresciaItaly
  2. 2.Department of Computer ScienceUniversity of RochesterRochesterUSA

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