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Constraint-Based Algorithm for Computing Temporal Invariants

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Logics in Artificial Intelligence (JELIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8761))

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Abstract

Automatically identified invariants are an important part of reductions of state-space reachability problems to SAT and related formalisms as a method of pruning the search space. No general algorithms for computing temporal invariants have been proposed before. Earlier algorithms restrict to unconditional actions and at-most-one invariants. We propose a powerful inductive algorithm for computing invariants for timed systems, showing that a wide range of timed modeling languages can be handled uniformly. The algorithm reduces the computation of timed invariants to a sequence of temporal logic consistency tests.

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References

  1. Bernardini, S., Smith, D.E.: Automatic synthesis of temporal invariants. In: Proceedings of the Ninth Symposium on Abstraction, Reformulation, and Approximation, SARA 2011, Parador de Cardona, Cardona, Catalonia, Spain, July 17-18. AAAI Press (2011)

    Google Scholar 

  2. Burch, J.R., Clarke, E.M., Long, D.E., MacMillan, K.L., Dill, D.L.: Symbolic model checking for sequential circuit verification. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 13(4), 401–424 (1994)

    Article  Google Scholar 

  3. Edelkamp, S., Helmert, M.: Exhibiting knowledge in planning problems to minimize state encoding length. In: Biundo, S., Fox, M. (eds.) ECP 1999. LNCS (LNAI), vol. 1809, pp. 135–147. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  4. Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61–124 (2003)

    MATH  Google Scholar 

  5. Gerevini, A., Schubert, L.: Inferring state constraints for domain-independent planning. In: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI 1998) and the 10th Conference on Innovative Applications of Artificial Intelligence (IAAI 1998), pp. 905–912. AAAI Press (1998)

    Google Scholar 

  6. Gerevini, A., Schubert, L.K.: Discovering state constraints in DISCOPLAN: Some new results. In: Proceedings of the 17th National Conference on Artificial Intelligence (AAAI 2000) and the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI 2000), pp. 761–767. AAAI Press (2000)

    Google Scholar 

  7. Haslum, P., Geffner, H.: Admissible heuristics for optimal planning. In: Chien, S., Kambhampati, S., Knoblock, C.A. (eds.) Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems, pp. 140–149. AAAI Press (2000)

    Google Scholar 

  8. Haslum, P., Geffner, H.: Heuristic planning with time and resources. In: Cesta, A. (ed.) Recent Advances in AI Planning, Sixth European Conference on Planning (ECP 2014), pp. 107–112. AAAI Press (2014)

    Google Scholar 

  9. Kautz, H., Selman, B.: Pushing the envelope: planning, propositional logic, and stochastic search. In: Proceedings of the 13th National Conference on Artificial Intelligence and the 8th Innovative Applications of Artificial Intelligence Conference, pp. 1194–1201. AAAI Press (1996)

    Google Scholar 

  10. Rintanen, J.: A planning algorithm not based on directional search. In: Cohn, A.G., Schubert, L.K., Shapiro, S.C. (eds.) Principles of Knowledge Representation and Reasoning: Proceedings of the Sixth International Conference (KR 1998), pp. 617–624. Morgan Kaufmann (1998)

    Google Scholar 

  11. Rintanen, J.: An iterative algorithm for synthesizing invariants. In: Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-2000) and the 12th Conference on Innovative Applications of Artificial Intelligence (IAAI-2000), pp. 806–811. AAAI Press (2000)

    Google Scholar 

  12. Rintanen, J.: Regression for classical and nondeterministic planning. In: Ghallab, M., Spyropoulos, C.D., Fakotakis, N. (eds.) ECAI 2008: Proceedings of the 18th European Conference on Artificial Intelligence, pp. 568–571. IOS Press (2008)

    Google Scholar 

  13. Smith, D.E., Weld, D.S.: Temporal planning with mutual exclusion reasoning. In: Dean, T. (ed.) Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 326–337. Morgan Kaufmann Publishers (1999)

    Google Scholar 

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Rintanen, J. (2014). Constraint-Based Algorithm for Computing Temporal Invariants. In: Fermé, E., Leite, J. (eds) Logics in Artificial Intelligence. JELIA 2014. Lecture Notes in Computer Science(), vol 8761. Springer, Cham. https://doi.org/10.1007/978-3-319-11558-0_50

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  • DOI: https://doi.org/10.1007/978-3-319-11558-0_50

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11557-3

  • Online ISBN: 978-3-319-11558-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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