Skip to main content Accessibility help
×
Home

A review of generalized planning

  • Sergio Jiménez (a1), Javier Segovia-Aguas (a2) and Anders Jonsson (a2)

Abstract

Generalized planning studies the representation, computation and evaluation of solutions that are valid for multiple planning instances. These are topics studied since the early days of AI. However, in recent years, we are experiencing the appearance of novel formalisms to compactly represent generalized planning tasks, the solutions to these tasks (called generalized plans) and efficient algorithms to compute generalized plans. The paper reviews recent advances in generalized planning and relates them to existing planning formalisms, such as planning with domain control knowledge and approaches for planning under uncertainty, that also aim at generality.

Copyright

References

Hide All
Albore, A., Palacios, H. & Geffner, H. 2009. A translation-based approach to contingent planning. In IJCAI.
Albore, A., Ramrez, M. & Geffner, H. 2011. Effective heuristics and belief tracking for planning with incomplete information. In ICAPS.
Alford, R., Kuter, U. & Nau, D. S. 2009. Translating htns to PDDL: a small amount of domain knowledge can go a long way. In IJCAI.
Alur, R., Bodik, R., Juniwal, G., Martin, M. M. K., Raghothaman, M., Seshia, S. A., Singh, R., Solar-Lezama, A., Torlak, E. & Udupa, A. 2015. Syntax-guided synthesis. Dependable Software Systems Engineering 40, 125.
Bacchus, F. & Kabanza., F. 2000. Using temporal logics to express search control knowledge for planning. Artificial Intelligence 116(1), 123191.
Bäckström, C., Jonsson, A. & Jonsson, P. 2014. Automaton plans. Journal of Artificial Intelligence Research 51, 255291.
Baier, J. A., Fritz, C. & McIlraith, S. A. 2007. Exploiting procedural domain control knowledge in state-of-the-art planners. In ICAPS.
Bernhard, N. 2000. On the compilability and expressive power of propositional planning formalisms. Journal of Artificial Intelligence Research 12, 271315.
Bonet, B. & Geffner, H. 2014. Belief tracking for planning with sensing: width, complexity and approximations. Journal of Artificial Intelligence Research 50, 923970.
Bonet, B. & Geffner, H. 2015. Policies that generalize: solving many planning problems with the same policy. In IJCAI.
Bonet, B., Palacios, H. & Geffner, H. 2010. Automatic derivation of finite-state machines for behavior control. In AAAI.
Borrajo, D., Roubickova, A. & Serina, I. 2015. Progress in case-based planning. ACM Computing Surveys (CSUR) 47(2), 35.
Botea, A., Enzenberger, M., Müller, M. & Schaeffer, J. 2005. Macro-ff: improving AI planning with automatically learned macro-operators. Journal of Artificial Intelligence Research 24, 581621.
Cimatti, A., Pistore, M., Roveri, M. & Traverso, P. 2003. Weak, strong, and strong cyclic planning via symbolic model checking. Artificial Intelligence 147(1–2), 3584.
Cimatti, A., Roveri, M. & Bertoli, P. 2004. Conformant planning via symbolic model checking and heuristic search. Artificial Intelligence 159(1–2), 127206.
Claßen, J., Engelmann, V., Lakemeyer, G. & Röger, G. 2008. Integrating golog and planning: an empirical evaluation. In Non-Monotonic Reasoning Workshop.
Clarke, E. M., Grumberg, O. & Peled, D. 1999. Model checking. MIT press.
Coles, A. & Smith, A. 2007. Marvin: a heuristic search planner with online macro-action learning. Journal of Artificial Intelligence Research 28, 119156.
Craven, M. & Slattery, S. 2001. Relational learning with statistical predicate invention: better models for hypertext. Machine Learning 43(1), 97119.
Cresswell, S. & Alexandra, M. 2004. Coddington. Compilation of LTL goal formulas into PDDL. In ECAI.
Domshlak, C. 2013. Fault tolerant planning: complexity and compilation. In ICAPS.
Fern, A., Khardon, R. & Tadepalli, P. 2011. The first learning track of the international planning competition. Machine Learning 84(1–2), 81107.
Fern, A., Yoon, S. & Givan, R. 2006. Approximate policy iteration with a policy language bias: solving relational markov decision processes. Journal of Artificial Intelligence Research 25, 75118.
Fikes, R. E., Hart, P. E. & Nilsson, N. J. 1972. Learning and executing generalized robot plans. Artificial intelligence 3, 251288.
Fox, M., Gerevini, A., Long, D. & Serina, I. 2006. Plan stability: replanning versus plan repair. In ICAPS.
Fox, M. & Long, D. 2003. Pddl2. 1: an extension to pddl for expressing temporal planning domains. Journal of Artificial Intelligence Research 20, 61124.
Francès, G. & Geffner, H. 2015. Modeling and computation in planning: better heuristics from more expressive languages. In ICAPS.
Francès, G. & Geffner, H. 2016. E-strips: existential quantification in planning and constraint satisfaction. In IJCAI.
Frances, G., Ramrez, M., Lipovetzky, N. & Geffner, H. 2017. Purely declarative action representations are overrated: classical planning with simulators. In IJCAI.
Fritz, C., Baier, J. A. & McIlraith, S. A. 2008. Congolog, sin trans: compiling congolog into basic action theories for planning and beyond. In KR.
Geffner, H. & Bonet, B. 2013. A concise introduction to models and methods for automated planning. Synthesis Lectures on Artificial Intelligence and Machine Learning 8(1), 1141.
Gerevini, A. & Long, D. 2005. Plan constraints and preferences in pddl3. The language of the fifth international planning competition. Technical Report, Department of Electronics for Automation, University of Brescia, 75.
Ghallab, M., Nau, D. & Traverso, P. 2004. Automated Planning: Theory and Practice. Elsevier.
Gulwani, S. 2011. Automating string processing in spreadsheets using input-output examples. In ACM SIGPLAN Notices 46, 317–330. ACM.
Gulwani, S., Hernandez-Orallo, J., Kitzelmann, E., Muggleton, S. H., Schmid, U. & Zorn, B. 2015. Inductive programming meets the real world. Communications of the ACM 58, 9099.
Hector, J. 2005. Levesque. Planning with loops. In IJCAI.
Helmert, M. 2006. The fast downward planning system. Journal of Artificial Intelligence Research 26, 191246.
Hoffmann, J. 2015. Simulated penetration testing: From dijkstra to turing test++. In ICAPS.
Hoffmann, J. & Brafman, R. I. 2006. Conformant planning via heuristic forward search: a new approach. Artificial Intelligence 170(6–7), 507541.
Hoffmann, J., Porteous, J. & Sebastia, L. 2004. Ordered landmarks in planning. Journal of Artificial Intelligence Research 22, 215278.
Howey, R., Long, D. & Fox, M. 2004. Val: automatic plan validation, continuous effects and mixed initiative planning using pddl. In ICTAI.
Hu, Y. & Giacomo, G. D. 2011. Generalized planning: synthesizing plans that work for multiple environments. In IJCAI.
Hu, Y. & Giacomo, G. D. 2013. A generic technique for synthesizing bounded finite-state controllers. In ICAPS.
Hu, Y. & Levesque, H. J. 2011. A correctness result for reasoning about one-dimensional planning problems. In IJCAI.
Ivankovic, F. & Haslum, P. 2015. Optimal planning with axioms. In IJCAI.
Jiménez, S. & Jonsson, A. 2015. Computing plans with control flow and procedures using a classical planner. In SOCS.
Jonsson, A. 2009. The role of macros in tractable planning. Journal of Artificial Intelligence Research 36, 471511.
Khardon, R. 1999. Learning action strategies for planning domains. Artificial Intelligence 113(1), 125148.
Kolobov, A. 2012. Planning with markov decision processes: an AI perspective. Synthesis Lectures on Artificial Intelligence and Machine Learning 6(1), 1210.
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. 2015. Human-level concept learning through probabilistic program induction. Science 350(6266), 13321338.
Leung, A., Sarracino, J. & Lerner, S. 2015. Interactive parser synthesis by example. In ACM SIGPLAN Notices, 50, 565–574. ACM.
Levesque, H. J., Reiter, R., Lespérance, Y., Lin, F. & Scherl, R. B. 1997. Golog: a logic programming language for dynamic domains. The Journal of Logic Programming 31(1–3), 5983.
Long, D. & Fox, M. 2003. The 3rd international planning competition: results and analysis. Journal of Artificial Intelligence Research 20, 159.
Lotinac, D., Segovia-Aguas, J., Jiménez, S. & Jonsson, A. 2016. Automatic generation of high-level state features for generalized planning. In IJCAI.
Lukás, C. 2010. Generation of macro-operators via investigation of action dependencies in plans. The Knowledge Engineering Review 25(3), 281297.
Marthi, B., Russell, S. J. & Wolfe, J. A. 2007. Angelic semantics for high-level actions. In ICAPS.
Martn, M. & Geffner, H. 2004. Learning generalized policies from planning examples using concept languages. Applied Intelligence 20(1), 919.
Mausam, & Kolobov, A. 2012. Planning with markov decision processes: an AI perspective. Morgan & Claypool Publishers.
McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D. & Wilkins, D. 1998. Pddl-the planning domain definition language.
Mitchell, T. M. 1982. Generalization as search. Artificial Intelligence 18, 203226.
Mitchell, T. M. 1997. Machine Learning, 1st edition. McGraw-Hill Inc.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G. & Petersen, S. 2015. Human-level control through deep reinforcement learning. Nature 518(7540), 529533.
Muggleton, S. 1999. Inductive logic programming: issues, results and the challenge of learning language in logic. Artificial Intelligence 114(1), 283296.
Muise, C. J., Belle, V. & McIlraith, S. A. 2014. Computing contingent plans via fully observable non-deterministic planning. In AAAI.
Muise, C., McIlraith, S. A. & Belle, V. 2014. Non-deterministic planning with conditional effects. In ICAPS.
Nau, D. S., Au, T.-C., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D. & Yaman, F. 2003. Shop2: An HTN planning system. Journal of Artificial Intelligence Research 20, 379404.
Newell, A., Shaw, J. C. & Simon, H. A. 1959. A general problem-solving program for a computer. Computers and Automation 8(7), 1016.
Palacios, H. & Geffner, H. 2009. Compiling uncertainty away in conformant planning problems with bounded width. Journal of Artificial Intelligence Research 35, 623675.
Pan, S. J. & Yang, Q. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 13451359.
Pednault, E. P. D. 1989. Adl: Exploring the middle ground between strips and the situation calculus. In KR.
Petrick, R. P. A. & Bacchus, F. 2004. Extending the knowledge-based approach to planning with incomplete information and sensing. In ICAPS.
Porco, A., Machado, A. & Bonet, B. 2011. Automatic polytime reductions of np problems into a fragment of strips. In ICAPS.
Pralet, C., Verfaillie, G., Lematre, M. & Infantes, G. 2010. Constraint-based controller synthesis in non-deterministic and partially observable domains. In ECAI.
Ross Quinlan, J. 1990. Learning logical definitions from relations. Machine Learning 5, 239266.
Ramírez, M. & Geffner, H. 2010. Probabilistic plan recognition using off-the-shelf classical planners. In AAAI.
Ramirez, M. & Geffner, H. 2016. Heuristics for planning, plan recognition and parsing. arXiv preprint arXiv:1605.05807.
Rintanen, J. 2012. Planning as satisfiability: heuristics. Artificial Intelligence Journal 193, 4586.
Rintanen, J. 2015. Impact of modeling languages on the theory and practice in planning research. In AAAI, 4052–4056.
Röger, G., Helmert, M. & Nebel, B. 2008. On the relative expressiveness of adl and golog: the last piece in the puzzle. In KR.
Rosa, T. D. L., Jiménez, S., Fuentetaja, R. & Borrajo, D. 2011. Scaling up heuristic planning with relational decision trees. Journal of Artificial Intelligence Research 40, 767813.
Sardina, S., Giacomo, G. D., Lespérance, Y. & Levesque, H. J. 2004. On the semantics of deliberation in indigolog from theory to implementation. Annals of Mathematics and Artificial Intelligence 41(2–4), 259299.
Scala, E., Ramirez, M., Haslum, P. & Thiebaux, S. 2016. Numeric planning with disjunctive global constraints via smt. In ICAPS.
Schwinghammer, J., Birkedal, L., Reus, B. & Yang, H. 2009. Nested hoare triples and frame rules for higher-order store. In International Workshop on Computer Science Logic.
Segovia-Aguas, J., Jiménez, S. & Jonsson, A. 2016a. Generalized planning with procedural domain control knowledge. In ICAPS.
Segovia-Aguas, J., Jiménez, S. & Jonsson, A. 2016b. Hierarchical finite state controllers for generalized planning. In IJCAI.
Segovia-Aguas, J., Jiménez, S. & Jonsson, A. 2017a. Generating context-free grammars using classical planning. In IJCAI.
Segovia-Aguas, J., Jiménez, S. & Jonsson, A. 2017b. Unsupervised classification of planning instances. In ICAPS.
Shivashankar, V., Kuter, U., Nau, D. & Alford, R. 2012. A hierarchical goal-based formalism and algorithm for single-agent planning. In AAMAS.
Slaney, J. & Thiébaux, S. 2001. Blocks world revisited. Artificial Intelligence 125(1), 119153.
Solar-Lezama, A., Tancau, L., Bodik, R., Seshia, S. & Saraswat, V. 2006. Combinatorial sketching for finite programs. ACM SIGOPS Operating Systems Review 40, 404415.
Srivastava, S., Immerman, N. & Zilberstein, S. 2011a. A new representation and associated algorithms for generalized planning. Artificial Intelligence 175(2), 615647.
Srivastava, S., Immerman, N., Zilberstein, S. & Zhang, T. 2011b. Directed search for generalized plans using classical planners. In ICAPS.
Thiébaux, S., Hoffmann, J. & Nebel, B. 2005. In defense of pddl axioms. Artificial Intelligence 168(1), 3869.
Torlak, E. & Bodik, R. 2013. Growing solver-aided languages with rosette. In ACM international symposium on New ideas, new paradigms, and reflections on programming & software, 135–152. ACM.
Utgoff, P. E. 1989. Incremental induction of decision trees. Machine Learning 4(2), 161186.
Vallati, M., Chrpa, L., Grzes, M., McCluskey, T. L., Roberts, M. & Sanner, S. 2015. The 2014 international planning competition: progress and trends. AI Magazine 36(3), 9098.
Veloso, M., Carbonell, J., Perez, A., Borrajo, D., Fink, E. & Blythe, J. 1995. Integrating planning and learning: the prodigy architecture. Journal of Experimental & Theoretical Artificial Intelligence 7(1), 81120.
Winner, E. & Veloso, M. 2003. Distill: learning domain-specific planners by example. In ICML.
Winner, E. & Veloso, M. 2007. Loopdistill: Learning looping domain-specific planners from example plans. In ICAPS, Workshop on Artificial Intelligence Planning and Learning.
Yoon, S., Fern, A. & Givan, R. 2008. Learning control knowledge for forward search planning. The Journal of Machine Learning Research 9, 683718.
Younes, H. L. S. & Littman, M. L. 2004. PPDDL1. 0: an extension to pddl for expressing planning domains with probabilistic effects. Technical Report, CMU-CS-04-162.

A review of generalized planning

  • Sergio Jiménez (a1), Javier Segovia-Aguas (a2) and Anders Jonsson (a2)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed