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Hybrid conditional planning using answer set programming



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.



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Albore, A., Palacios, H. and Geffner, H. 2007. Fast and informed action selection for planning with sensing. In Proc. of CAEPIA, 1–10.
Albore, A., Palacios, H. and Geffner, H. 2009. A translation-based approach to contingent planning. In Proc. of IJCAI, 1623–1628.
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.
Bonet, B. and Geffner, H. 2011. Planning under partial observability by classical replanning: Theory and experiments. In Proc. of IJCAI, 1936–1941.
Brafman, R. I. and Shani, G. 2012. Replanning in domains with partial information and sensing actions. JAIR 45, 565600.
Brewka, G., Eiter, T. and Truszczynski, M. 2016. Answer set programming: An introduction to the special issue. AI Magazine 37, 3, 56.
Bryce, D., Kambhampati, S. and Smith, D. E. 2006. Planning graph heuristics for belief space search. JAIR 26, 3599.
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.
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.
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.
Erdem, E., Gelfond, M. and Leone, N. 2016a. Applications of answer set programming. AI Magazine 37, 3, 5368.
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.
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, 319349.
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.
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.
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.
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365385.
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N. and Turner, H. 2004. Nonmonotonic causal theories. Artificial Intelligence 153, 49104.
Goldman, R. P. and Boddy, M. S. 1996. Expressive planning and explicit knowledge. In Proc. of AIPS, 110–117.
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.
Hauser, K. and Latombe, J.-C. 2009. Integrating task and PRM motion planning: Dealing with many infeasible motion planning queries. In BTAMP at ICAPS.
Hertle, A., Dornhege, C., Keller, T. and Nebel, B. 2012. Planning with semantic attachments: An object-oriented view. In Proc. of ECAI, 402–407.
Hoffmann, J. and Brafman, R. I. 2005. Contingent planning via heuristic forward search with implicit belief states. In Proc. of ICAPS, 71–80.
Kaelbling, L. P. and Lozano-Pérez, T. 2013. Integrated task and motion planning in belief space. IJRR 32, 9–10, 11941227.
Komarnitsky, R. and Shani, G. 2016. Computing contingent plans using online replanning. In Proc. of AAAI, 3159–3165.
Kuffner, J. Jr and LaValle, S. 2000. RRT-connect: An efficient approach to single-query path planning. In Proc. of ICRA, 995–1001.
Lagriffoul, F., Dimitrov, D., Bidot, J., Saffiotti, A. and Karlsson, L. 2014. Efficiently combining task and motion planning using geometric constraints. IJRR 33, 14, 17261747.
Maliah, S., Brafman, R. I., Karpas, E. and Shani, G. 2014. Partially observable online contingent planning using landmark heuristics. In Proc. of ICAPS.
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.
Nouman, A., Yalciner, I. F., Erdem, E. and Patoglu, V. 2016. Experimental evaluation of hybrid conditional planning for service robotics. In Proc. of ISER.
Peot, M. A. and Smith, D. E. 1992. Conditional nonlinear planning. In Proc. of AIPS, 189–197.
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.
Plaku, E. 2012. Planning in discrete and continuous spaces: From LTL tasks to robot motions. In Proc. of TAROS, 331–342.
Pryor, L. and Collins, G. 1996. Planning for contingencies: A decision-based approach. JAIR 4, 287339.
Son, T. C. and Baral, C. 2001. Formalizing sensing actions: A transition function based approach. Artificial Intelligence 125, 1–2, 1991.
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.
To, S. T., Son, T. C. and Pontelli, E. 2011. Contingent planning as AND/OR forward search with disjunctive representation. In Proc. of ICAPS.
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, 377450.
Warren, D. H. D. 1976. Generating conditional plans and programs. In Proc. of AISB, 344–354.
Weld, D. S., Anderson, C. R. and Smith, D. E. 1998. Extending graphplan to handle uncertainty & sensing actions. In Proc. of AAAI, 897–904.
Weyhrauch, R. W. 1978. Prolegomena to a Theory of Formal Reasoning. Technical report, Stanford University.


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Hybrid conditional planning using answer set programming



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