Abstract
The high complexity of planning with partial observability has motivated to find compact representations of belief state (sets of states) that reduce their size exponentially, including the 3-valued literal-based approximations by Baral et al. and tag-based approximations by Palacios and Geffner.
We present a generalization of 3-valued literal-based approximations, and an algorithm that analyzes a succinctly represented planning problem to derive a set of formulas the truth of which accurately represents any reachable belief state. This set is not limited to literals and can contain arbitrary formulas. We demonstrate that a factored representation of belief states based on this analysis enables fully automated reduction of conformant planning problems to classical planning, bypassing some of the limitations of earlier approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The left-hand side of this conditional effect can be simplified by replacing all occurrences of \(\phi \) by \(\top \), as the effect does something only if \(\phi \) is true when the action is taken. This modification is is needed to maximize Graphplan-style [3] parallelism.
References
Bailey, J., Stuckey, P.J.: Discovery of minimal unsatisfiable subsets of constraints using hitting set dualization. In: Hermenegildo, M.V., Cabeza, D. (eds.) PADL 2005. LNCS, vol. 3350, pp. 174–186. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30557-6_14
Baral, C., Kreinovich, V., Trejo, R.: Computational complexity of planning and approximate planning in the presence of incompleteness. Artif. Intell. 122(1), 241–267 (2000)
Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90(1–2), 281–300 (1997)
Bryant, R.E.: Symbolic Boolean manipulation with ordered binary decision diagrams. ACM Comput. Surv. 24(3), 293–318 (1992)
Burch, J.R., Clarke, E.M., Long, D.E., MacMillan, K.L., Dill, D.L.: Symbolic model checking for sequential circuit verification. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 13(4), 401–424 (1994)
Bylander, T.: The computational complexity of propositional STRIPS planning. Artif. Intell. 69(1–2), 165–204 (1994)
Geffner, T., Geffner, H.: Compact policies for non-deterministic fully observable planning as sat. In: ICAPS 2018. Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling, pp. 88–96. AAAI Press (2018)
Haslum, P., Jonsson, P.: Some results on the complexity of planning with incomplete information. In: Biundo, S., Fox, M. (eds.) ECP 1999. LNCS (LNAI), vol. 1809, pp. 308–318. Springer, Heidelberg (2000). https://doi.org/10.1007/10720246_24
Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. J. Artif. Intell. Res. 14, 253–302 (2001)
Liffiton, M.H., Sakallah, K.A.: On finding all minimally unsatisfiable subformulas. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 173–186. Springer, Heidelberg (2005). https://doi.org/10.1007/11499107_13
Palacios, H., Geffner, H.: Compiling uncertainty away in conformant planning problems with bounded width. J. Artif. Intell. Res. 35, 623–675 (2009)
Rintanen, J.: Complexity of planning with partial observability. In: ICAPS 2004. Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling, pp. 345–354. AAAI Press (2004)
Rintanen, J.: Regression for classical and nondeterministic planning. In: ECAI 2008. Proceedings of the 18th European Conference on Artificial Intelligence, pp. 568–571. IOS Press (2008)
Rintanen, J.: Planning as satisfiability: heuristics. Artif. Intell. 193, 45–86 (2012)
To, S., Pontelli, E., Son, T.: A conformant planner with explicit disjunctive representation of belief states. In: Proceedings of the 19th International Conference on Automated Planning and Scheduling, pp. 305–312. AAAI Press (2009)
To, S., Son, T., Pontelli, E.: On the use of prime implicates in conformant planning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1205–1210. AAAI Press (2010)
To, S.T., Son, T.C., Pontelli, E.: A new approach to conformant planning using CNF. In: Proceedings of the 20th International Conference on Automated Planning and Scheduling, pp. 169–176. AAAI Press (2010)
To, S.T., Son, T.C., Pontelli, E.: A generic approach to planning in the presence of incomplete information: theory and implementation. Artif. Intell. 227, 1–51 (2015)
Tu, P.H., Son, T.C., Baral, C.: Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming. Theory Pract. Logic Program. 7, 1–74 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fadnis, S., Rintanen, J. (2022). Generalized 3-Valued Belief States in Conformant Planning. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-20862-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20861-4
Online ISBN: 978-3-031-20862-1
eBook Packages: Computer ScienceComputer Science (R0)