Article contents
The RANTANPLAN planner: system description
Published online by Cambridge University Press: 22 December 2016
Abstract
RANTANPLAN is a numeric planning solver that takes advantage of recent advances in satisfiability modulo theories. It extends reduction to SAT approaches with an easy and efficient handling of numeric fluents using background theories. In this paper, we describe the design choices and features of RANTANPLAN, especially, how numeric reasoning is integrated in the system. We also provide experimental results showing that RANTANPLAN is competitive with existing exact numeric planners.
- Type
- Articles
- Information
- The Knowledge Engineering Review , Volume 31 , Special Issue 5: Constraint Satisfaction for Planning and Scheduling , November 2016 , pp. 452 - 464
- Copyright
- © Cambridge University Press, 2016
References
Barrett, C., Sebastiani, R., Seshia, S. & Tinelli, C.
2009. Satisfiability modulo theories. In Handbook of Satisfiability, Biere, A., Heule, M., van Maaren, H. & Walsh, T. (eds). 185, Chapter 26. IOS Press, 825–885.Google Scholar
Barták, R. & Toropila, D.
2010. Solving sequential planning problems via constraint satisfaction. Fundamenta Informaticae
99(2), 125–145.Google Scholar
Belouaer, L. & Maris, F.
2012. SMT spatio-temporal planning. In ICAPS Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems (COPLAS 2012), 6–15.Google Scholar
Bofill, M., Espasa, J. & Villaret, M.
2014. Efficient SMT encodings for the petrobras domain. In Proceedings of the 13th International Workshop on Constraint Modelling and Reformulation (ModRef 2014), 68–84.Google Scholar
Bofill, M., Espasa, J. & Villaret, M.
2016. A semantic notion of interference for planning modulo theories. In Proceedings of the Twenty-Sixth International Conference on Automated Planning and Scheduling, ICAPS 2016, 56–64.Google Scholar
Dovier, A., Formisano, A. & Pontelli, E.
2010. Multivalued action languages with constraints in CLP (FD). Theory and Practice of Logic Programming
10(2), 167–235.Google Scholar
Dutertre, B. & De Moura, L.
2006. The Yices SMT solver. Technical report, Computer Science Laboratory, SRI International. http://yices.csl.sri.com.Google Scholar
Fox, M. & Long, D.
2003. PDDL2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research
20, 61–124.Google Scholar
Frisch, A. M. & Giannaros, P. A.
2010. SAT encodings of the at-most-fc constraint. Some old, some new, some fast, some slow. In 10th International Workshop on Constraint Modelling and Reformulation (ModRef 2010).Google Scholar
Gerevini, A. E., Saetti, A. & Serina, I.
2008. An approach to efficient planning with numerical fluents and multi-criteria plan quality. Artificial Intelligence
172(8), 899–944.Google Scholar
Gregory, P., Long, D., Fox, M. & Beck, J. C.
2012. Planning modulo theories: extending the planning paradigm. In Twenty-Second International Conference on Automated Planning and Scheduling (ICAPS 2012). AAAI.Google Scholar
Hoffmann, J.
2003. The Metric-FF planning system: translating ‘ignoring delete lists’ to numeric state variables. Journal of Artificial Intelligence Research
20, 291–341.Google Scholar
Hoffmann, J., Gomes, C. P., Selman, B. & Kautz, H. A.
2007. SAT encodings of state-space reachability problems in numeric domains. In 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), 1918–1923.Google Scholar
Kautz, H. & Selman, B.
1992. Planning as satisfiability. In 10th European Conference on Artificial Intelligence (ECAI 92), 359–363. John Wiley & Sons Inc.Google Scholar
Kautz, H. & Walser, J. P.
1999. State-space planning by integer optimization. In AAAI/IAAI, 526–533.Google Scholar
Kautz, H. A., McAllester, D. A. & Selman, B.
1996. Encoding plans in propositional logic. In Fifth International Conference on Principles of Knowledge Representation and Reasoning (KR 96), 374–384.Google Scholar
Rintanen, J.
2012. Planning as satisfiability: heuristics. Artificial Intelligence
193, 45–86.Google Scholar
Rintanen, J., Heljanko, K. & Niemelä, I.
2006. Planning as satisfiability: parallel plans and algorithms for plan search. Artificial Intelligence
170(12–13), 1031–1080.Google Scholar
Wolfman, S. A. & Weld, D. S.
1999. The LPSAT engine & its application to resource planning. In Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 99), 310–317.Google Scholar
- 2
- Cited by