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Hybrid conditional planning using answer set programming

Published online by Cambridge University Press:  22 August 2017

IBRAHIM FARUK YALCINER ,
AHMED NOUMAN ,
VOLKAN PATOGLU  and
ESRA ERDEM Open the ORCID record for ESRA ERDEM [Opens in a new window]
IBRAHIM FARUK YALCINER
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey (e-mail: fyalciner@sabanciuniv.edu, ahmednouman@sabanciuniv.edu, vpatoglu@sabanciuniv.edu, esraerdem@sabanciuniv.edu)
AHMED NOUMAN
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey (e-mail: fyalciner@sabanciuniv.edu, ahmednouman@sabanciuniv.edu, vpatoglu@sabanciuniv.edu, esraerdem@sabanciuniv.edu)
VOLKAN PATOGLU
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey (e-mail: fyalciner@sabanciuniv.edu, ahmednouman@sabanciuniv.edu, vpatoglu@sabanciuniv.edu, esraerdem@sabanciuniv.edu)
ESRA ERDEM
Affiliation:
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey (e-mail: fyalciner@sabanciuniv.edu, ahmednouman@sabanciuniv.edu, vpatoglu@sabanciuniv.edu, esraerdem@sabanciuniv.edu)
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Abstract

We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains.

Keywords

Conditional planninghybrid planninganswer set programmingcognitive robotics

Information

Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 17 , Special Issue 5-6: 33rd International Conference on Logic Programming , September 2017 , pp. 1027 - 1047
Copyright
Copyright © Cambridge University Press 2017 

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References

Albore, A., Palacios, H. and Geffner, H. 2007. Fast and informed action selection for planning with sensing. In Proc. of CAEPIA, 1–10.Google Scholar
Albore, A., Palacios, H. and Geffner, H. 2009. A translation-based approach to contingent planning. In Proc. of IJCAI, 1623–1628.Google Scholar
Baral, C., Kreinovich, V. and Trejo, R. 1999. Computational complexity of planning and approximate planning in presence of incompleteness. In Proc. of IJCAI, 948–955.Google Scholar
Bonet, B. and Geffner, H. 2011. Planning under partial observability by classical replanning: Theory and experiments. In Proc. of IJCAI, 1936–1941.Google Scholar
Brafman, R. I. and Shani, G. 2012. Replanning in domains with partial information and sensing actions. JAIR 45, 565–600.CrossRefGoogle Scholar
Brewka, G., Eiter, T. and Truszczynski, M. 2016. Answer set programming: An introduction to the special issue. AI Magazine 37, 3, 5–6.CrossRefGoogle Scholar
Bryce, D., Kambhampati, S. and Smith, D. E. 2006. Planning graph heuristics for belief space search. JAIR 26, 35–99.CrossRefGoogle Scholar
Caldiran, O., Haspalamutgil, K., Ok, A., Palaz, C., Erdem, E. and Patoglu, V. 2009. Bridging the gap between high-level reasoning and low-level control. In Proc. of LPNMR.CrossRefGoogle Scholar
Dantam, N. T., Kingston, Z. K., Chaudhuri, S. and Kavraki, L. E. 2016. Incremental task and motion planning: A constraint-based approach. In Proc. of RSS.Google Scholar
Eiter, T., Ianni, G., Schindlauer, R. and Tompits, H. 2005. A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In Proc. of IJCAI, 90–96.Google Scholar
Erdem, E., Gelfond, M. and Leone, N. 2016a. Applications of answer set programming. AI Magazine 37, 3, 53–68.CrossRefGoogle Scholar
Erdem, E., Haspalamutgil, K., Palaz, C., Patoglu, V. and Uras, T. 2011. Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation. In Proc. of ICRA.CrossRefGoogle Scholar
Erdem, E., Patoglu, V. and Schüller, P. 2016. A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks. AI Communications 29, 2, 319–349.CrossRefGoogle Scholar
Erol, K., Nau, D. S. and Subrahmanian, V. S. 1995. Complexity, decidability and undecidability results for domain-independent planning. Artificial Intelligence 76, 1–2, 75-88.CrossRefGoogle Scholar
Gaschler, A., Petrick, R. P., Giuliani, M., Rickert, M. and Knoll, A. 2013. KVP: A knowledge of volumes approach to robot task planning. In Proc. of IROS.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2014. Clingo = ASP + control: Preliminary report. In Technical Communications of ICLP, vol. 14(4–5). TPLP, Online supplement.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.CrossRefGoogle Scholar
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N. and Turner, H. 2004. Nonmonotonic causal theories. Artificial Intelligence 153, 49–104.CrossRefGoogle Scholar
Goldman, R. P. and Boddy, M. S. 1996. Expressive planning and explicit knowledge. In Proc. of AIPS, 110–117.Google Scholar
Gravot, F., Cambon, S. and Alami, R. 2005. aSyMov:A Planner that deals with intricate symbolic and geometric problems. In Proc. of ISRR, 100–110.Google Scholar
Hauser, K. and Latombe, J.-C. 2009. Integrating task and PRM motion planning: Dealing with many infeasible motion planning queries. In BTAMP at ICAPS.Google Scholar
Hertle, A., Dornhege, C., Keller, T. and Nebel, B. 2012. Planning with semantic attachments: An object-oriented view. In Proc. of ECAI, 402–407.Google Scholar
Hoffmann, J. and Brafman, R. I. 2005. Contingent planning via heuristic forward search with implicit belief states. In Proc. of ICAPS, 71–80.Google Scholar
Kaelbling, L. P. and Lozano-Pérez, T. 2013. Integrated task and motion planning in belief space. IJRR 32, 9–10, 1194–1227.Google Scholar
Komarnitsky, R. and Shani, G. 2016. Computing contingent plans using online replanning. In Proc. of AAAI, 3159–3165.Google Scholar
Kuffner, J. Jr and LaValle, S. 2000. RRT-connect: An efficient approach to single-query path planning. In Proc. of ICRA, 995–1001.CrossRefGoogle Scholar
Lagriffoul, F., Dimitrov, D., Bidot, J., Saffiotti, A. and Karlsson, L. 2014. Efficiently combining task and motion planning using geometric constraints. IJRR 33, 14, 1726–1747.Google Scholar
Maliah, S., Brafman, R. I., Karpas, E. and Shani, G. 2014. Partially observable online contingent planning using landmark heuristics. In Proc. of ICAPS.CrossRefGoogle Scholar
Muise, C. J., Belle, V. and McIlraith, S. A. 2014. Computing contingent plans via fully observable non-deterministic planning. In Proc. of AAAI, 2322–2329.Google Scholar
Nouman, A., Yalciner, I. F., Erdem, E. and Patoglu, V. 2016. Experimental evaluation of hybrid conditional planning for service robotics. In Proc. of ISER.CrossRefGoogle Scholar
Peot, M. A. and Smith, D. E. 1992. Conditional nonlinear planning. In Proc. of AIPS, 189–197.Google Scholar
Petrick, R. P. A. and Bacchus, F. 2002. A knowledge-based approach to planning with incomplete information and sensing. In Proc. of AIPS, 212–222.Google Scholar
Plaku, E. 2012. Planning in discrete and continuous spaces: From LTL tasks to robot motions. In Proc. of TAROS, 331–342.CrossRefGoogle Scholar
Pryor, L. and Collins, G. 1996. Planning for contingencies: A decision-based approach. JAIR 4, 287–339.CrossRefGoogle Scholar
Son, T. C. and Baral, C. 2001. Formalizing sensing actions: A transition function based approach. Artificial Intelligence 125, 1–2, 19–91.CrossRefGoogle Scholar
Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S. and Abbeel, P. 2014. Combined task and motion planning through an extensible planner-independent interface layer. In Proc. of ICRA.CrossRefGoogle Scholar
To, S. T., Son, T. C. and Pontelli, E. 2011. Contingent planning as AND/OR forward search with disjunctive representation. In Proc. of ICAPS.CrossRefGoogle Scholar
Tu, P. H., Son, T. C. and Baral, C. 2007. Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming. TPLP 7, 4, 377–450.Google Scholar
Warren, D. H. D. 1976. Generating conditional plans and programs. In Proc. of AISB, 344–354.Google Scholar
Weld, D. S., Anderson, C. R. and Smith, D. E. 1998. Extending graphplan to handle uncertainty & sensing actions. In Proc. of AAAI, 897–904.Google Scholar
Weyhrauch, R. W. 1978. Prolegomena to a Theory of Formal Reasoning. Technical report, Stanford University.CrossRefGoogle Scholar
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