Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-14T05:49:34.007Z Has data issue: false hasContentIssue false
  • English
  • Français

Planning with Incomplete Information in Quantified Answer Set Programming

Published online by Cambridge University Press:  24 September 2021

JORGE FANDINNO Open the ORCID record for JORGE FANDINNO [Opens in a new window] ,
FRANCOIS LAFERRIERE ,
JAVIER ROMERO Open the ORCID record for JAVIER ROMERO [Opens in a new window] ,
TORSTEN SCHAUB Open the ORCID record for TORSTEN SCHAUB [Opens in a new window]  and
TRAN CAO SON Open the ORCID record for TRAN CAO SON [Opens in a new window]
JORGE FANDINNO
Affiliation:
Omaha State University, USA University of Potsdam, Germany (e-mail: jfandinno@unomaha.edu)
FRANCOIS LAFERRIERE
Affiliation:
University of Potsdam, Germany (e-mails: francois@cs.uni-potsdam.de, javier@cs.uni-potsdam.de, torsten@cs.uni-potsdam.de)
JAVIER ROMERO
Affiliation:
University of Potsdam, Germany (e-mails: francois@cs.uni-potsdam.de, javier@cs.uni-potsdam.de, torsten@cs.uni-potsdam.de)
TORSTEN SCHAUB
Affiliation:
University of Potsdam, Germany (e-mails: francois@cs.uni-potsdam.de, javier@cs.uni-potsdam.de, torsten@cs.uni-potsdam.de)
TRAN CAO SON
Affiliation:
New Mexico State University, USA (e-mail: tson@cs.nmsu.edu)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.

Keywords

answer set programmingplanningquantified logics
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 5: 37th International Conference on Logic Programming Special Issue I , September 2021 , pp. 663 - 679
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

References

Amendola, G., Ricca, F. and Truszczynski, M. 2019. Beyond NP: quantifying over answer sets. Theory and Practice of Logic Programming 19, 5–6, 705–721.CrossRefGoogle Scholar
Biere, A., Lonsing, F. and Seidl, M. 2011. Blocked clause elimination for QBF. In Proceedings of CADE’11, Lecture Notes in Computer Science, vol. 6803. Springer-Verlag, 101–115.Google Scholar
Cabalar, P., Kaminski, R., Schaub, T. and Schuhmann, A. 2018. Temporal answer set programming on finite traces. Theory and Practice of Logic Programming 18, 3–4, 406–420.CrossRefGoogle Scholar
Castellini, C., Giunchiglia, E. and Tacchella, A. 2003. SAT-based planning in complex domains: Concurrency, constraints and nondeterminism. Artificial Intelligence 147, 1–2, 85–117.CrossRefGoogle Scholar
Davis-Mendelow, S., Baier, J. and McIlraith, S. 2013. Assumption-based planning: Generating plans and explanations under incomplete knowledge. In Proceedings of AAAI 2013, desJardins, M. and Littman, M., Eds. AAAI Press, 209–216.Google Scholar
Eiter, T., Faber, W., Leone, N., Pfeifer, G. and Polleres, A. 2003. A logic programming approach to knowledge-state planning. Artificial Intelligence 144, 1–2, 157–211.CrossRefGoogle Scholar
Fandinno, J., Mishra, S., Romero, J. and Schaub, T. 2020. Answer set programming made easy. In Proceedings of ASPOCP 2020, Hecher, M. and Zangari, J., Eds.Google Scholar
Gelfond, M. and Lifschitz, V. 1998. Action languages. Electronic Transactions on Artificial Intelligence 3, 6, 193–210.Google Scholar
Giunchiglia, E., Marin, P. and Narizzano, M. 2009. Reasoning with quantified Boolean formulas. In Handbook of Satisfiability, Biere, A., Heule, M., van Maaren, H. and Walsh, T., Eds. Frontiers in Artificial Intelligence and Applications, vol. 185. IOS Press, Chapter 24, 761–780.Google Scholar
Janhunen, T. 2004. Representing normal programs with clauses. In Proceedings of ECAI 2004, López de Mántaras, R. and Saitta, L., Eds. IOS Press, 358–362.Google Scholar
Janota, M. and Marques-Silva, J. 2015. Solving QBF by clause selection. In Proceedings of IJCAI’15, Yang, Q. and Wooldridge, M., Eds. AAAI Press, 325–331.Google Scholar
Lifschitz, V. 2002. Answer set programming and plan generation. Artificial Intelligence 138, 1–2, 39–54.CrossRefGoogle Scholar
Lifschitz, V. and Turner, H. 1994. Splitting a logic program. In Proceedings of the Eleventh International Conference on Logic Programming. MIT Press, 23–37.Google Scholar
Lonsing, F. and Egly, U. 2017. DepQBF 6.0: A search-based QBF solver beyond traditional QCDCL. In Proceedings of CADE 2017. Springer-Verlag, 371–384.Google Scholar
Lonsing, F. and Egly, U. 2019. QRATPre+: Effective QBF preprocessing via strong redundancy properties. In Proceedings of SAT 2019. Springer-Verlag, 203–210.Google Scholar
Mayer-Eichberger, V. and Saffidine, A. 2020. Positional games and QBF: the corrective encoding. In Proceedings of SAT 2020. Springer-Verlag, 447–463.Google Scholar
Niemelä, I. 2008. Answer set programming without unstratified negation. In Proceedings of ICLP 2008. Springer-Verlag, 88–92.Google Scholar
Palacios, H. and Geffner, H. 2005. Mapping conformant planning into SAT through compilation and projection. In Proceedings of CAEPIA 2005. Springer-Verlag, 311–320.Google Scholar
Palacios, H. and Geffner, H. 2009. Compiling uncertainty away in conformant planning problems with bounded width. Journal of Artificial Intelligence Research 35, 623–675.CrossRefGoogle Scholar
Peitl, T., Slivovsky, F. and Szeider, S. 2019. Dependency learning for QBF. Journal of Artificial Intelligence Research 65, 180–208.CrossRefGoogle Scholar
Rabe, M. N. and Tentrup, L. 2015. CAQE: A certifying QBF solver. In Proceedings of FMCAD 2015, Kaivola, R. and Wahl, T., Eds. IEEE Computer Society Press, 136–143.Google Scholar
Rintanen, J. 1999. Constructing conditional plans by a theorem-prover. Journal of Artificial Intelligence Research 10, 323–352.CrossRefGoogle Scholar
Son, T., Tu, P., Gelfond, M. and Morales, A. 2005. An approximation of action theories of and its application to conformant planning. In Proceedings of LPNMR 2005. Springer-Verlag, 172–184.Google Scholar
Tu, P., Son, T. and Baral, C. 2007. Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming. Theory and Practice of Logic Programming 7, 4, 377–450.CrossRefGoogle Scholar
Tu, P., Son, T., Gelfond, M. and Morales, A. 2011. Approximation of action theories and its application to conformant planning. Artificial Intelligence 175, 1, 79–119.CrossRefGoogle Scholar
Turner, H. 2002. Polynomial-length planning spans the polynomial hierarchy. In Proceedings of JELIA 2002. Springer-Verlag, 111–124.Google Scholar
Wimmer, R., Reimer, S., Marin, P. and Becker, B. 2017. HQSpre – an effective preprocessor for QBF and DQBF. In Proceedings of TACAS 2017. Springer-Verlag, 373–390.Google Scholar
Yalciner, I., Nouman, A., Patoglu, V. and Erdem, E. 2017. Hybrid conditional planning using answer set programming. Theory and Practice of Logic Programming 17, 5–6, 1027–1047.CrossRefGoogle Scholar
Supplementary material: PDF

Fandinno et al. supplementary material

Fandinno et al. supplementary material

Download Fandinno et al. supplementary material(PDF)
PDF 210.9 KB