article

Planning in highly dynamic environments: an anytime approach for planning under time constraints

Authors:

E. OnaindíaAuthors Info & Claims

Applied Intelligence, Volume 29, Issue 1

Pages 90 - 109

https://doi.org/10.1007/s10489-007-0083-x

Published: 01 August 2008 Publication History

Abstract

In this paper, we present a novel and domain-independent planner aimed at working in highly dynamic environments with time constraints. The planner follows the anytime principles: a first solution can be quickly computed and the quality of the final plan is improved as long as time is available. This way, the planner can provide either fast reactions or very good quality plans depending on the demands of the environment. As an on-line planner, it also offers important advantages: our planner allows the plan to start its execution before it is totally generated, unexpected events are efficiently tackled during execution, and sensing actions allow the acquisition of required information in partially observable domains. The planning algorithm is based on problem decomposition and relaxation techniques. The traditional relaxed planning graph has been adapted to this on-line framework by considering information about sensing actions and action costs. Results also show that our planner is competitive with other top-performing classical planners.

References

[1]

Ambite JL, Knoblock CA (2001) Planning by rewriting. J Artif Intell Res 15:207-261.

Digital Library

[2]

Benton J, Sanchez R, Do M, Kambhampati S (2005) Partial satisfaction planning. In: Workshop on decision making in adversarial domains, http://www.cs.umd.edu/~nau/adversarial/

[3]

Blum A, Furst M (1997) Fast planning through planning graph analysis. Artif Intell 90:281-300.

Digital Library

[4]

Bonet B, Geffner H (2001) Planning as heuristic search. Artif Intell J 129:5-33.

Digital Library

[5]

Briggs W, Cook DJ (1999) Anytime planning for optimal tradeoff between deliberative and reactive planning. In: Proceedings of the 1999 Florida AI research symposium (FLAIRS-99), pp 367-370.

[6]

Castillo L, Fdez-Olivares J, Gonzalez A (2001) On the adequacy of hierarchical planning characteristics for real-world problem solving. In: European conference on planning (ECP-2001), pp 169-180.

[7]

Chapman D (1987) Planning for conjunctive goals. Artif Intell 32:333-377.

Digital Library

[8]

Chen Y, Hsu CW, Wah BW (2004) SGPlan: Subgoal partitioning and resolution in planning. In: IPC-4 booklet (ICAPS).

[9]

Chien S, Knight R, Stechert A, Sherwood R, Rabideau G (2000) Using iterative repair to improve the responsiveness of planning and scheduling. In: Proceedings of the 5th international conference on artificial intelligence planning and scheduling (AIPS).

[10]

Chun SB (1999) Wargaming. Air University Library, Maxwell AFB.

[11]

Dean T, Kaelbling LP, Kirman J, Nicholson A (1995) Planning under time constraints in stochastic domains. Artif Intell 76:35- 74.

Digital Library

[12]

Dias MB, Lemai S, Muscettola N (2003) A real-time rover executive based on model-based reactive planning. In: 7th international symposium on artificial intelligence, robotics and automation in space.

[13]

Do MB, Kambhampati S (2003) Sapa: A multi-objective metric temporal planner. J Artif Intell Res 20:155-194.

Digital Library

[14]

Drummond M, Bresina J, Kedar S (1991) The entropy reduction engine: integrating planning, scheduling and control. SIGART Bull 2:61-65.

Digital Library

[15]

Fikes RE, Nilsson NJ (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2:189- 208.

[16]

Fox M, Long D (2003) PDDL2.1: an extension to PDDL for expressing temporal planning domains. J Artif Intell Res 20:61-124.

[17]

Gerevini A, Saetti A, Serina I (2003) Planning through stochastic local search and temporal action graphs in LPG. J Artif Intell Res 20:239-290.

[18]

Gerevini A, Saetti A, Serina I, Toninelli P (2005) Fast planning in domains with derived predicates: an approach based on rule-action graphs and local search. In: Proceedings of the twentieth national conference on artificial intelligence (AAAI-05), pp 1157-1162.

[19]

Gervasio M, Iba W, Langley P, Sage S (1998) Interactive adaptation for crisis response. In: AIPS-98 workshop on interactive and collaborative planning, pp 29-36.

[20]

Ghallab M, Nau D, Traverso P (2004) Automated task planning. Theory and practice. Kaufmann, Los Altos.

[21]

Gil Y, Deelman E, Blythe J, Kesselman C, Tangmunarunkit H (2004) Artificial intelligence and grids: workflow planning and beyond. IEEE Intell Syst, special issue on E-science, pp 26-33.

[22]

Ginsberg ML (1989) Universal planning: an (almost) universally bad idea. AI Mag 10:40-44.

Digital Library

[23]

Hawes N (2001) Anytime planning for agent behavior. In: Proceedings of the 12th workshop of PLANSIG, pp 157-166.

[24]

Hoffman J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253-302.

[25]

Hoffman J (2003) The metric-FF planning system: translating 'ignoring delete lists' to numeric state variables. J Artif Intell Res 20:291-341.

[26]

Knoblock CA (2003) Deploying information agents on the web. In: Proceedings of the 18th international joint conference on artificial intelligence (IJCAI), pp 1580-1586.

[27]

Koehler J, Nebel B, Hoffmann J, Dimopoulos Y (1997) Extending planning graphs to an ADL subset. In: Proceedings of the fourth European conference in planning, pp 273-285.

[28]

Koehler J, Hoffmann J (2000) On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm. J Artif Intell Res 12:338-386.

[29]

Koenig S (2001) Agent-centered search. Artif Intell Mag 22:109- 131.

Digital Library

[30]

Koenig S, Furcy D, Bauer C (2002) Heuristic search-based replanning. In: International conference on artificial intelligence planning and scheduling (AIPS), pp 310-317.

[31]

Korf RE (1990) Real-time heuristic search. Artif Intell 42:189- 211.

Digital Library

[32]

Korf RE (1990) Depth-limited search for real-time problem solving. J Real-Time Syst 2:7-24.

Digital Library

[33]

Lemai S, Ingrand F (2004) Interleaving temporal planning and execution in robotics domains. In: Proceedings of the national conference on artificial intelligence (AAAI).

[34]

Littman ML (1997) Probabilistic propositional planning: representations and complexity. In: Proceedings of the fourteenth national conference on artificial intelligence, pp 748-754.

[35]

Matellan V, Borrajo D (1998) Combining classical and reactive planning: the ABC² model. In: AIPS'98 workshop on integrating planning, scheduling and execution in dynamic and uncertain environments, pp 121-126.

[36]

Nareyek A (2004) Artificial intelligence in computer games-- state of the art and future directions. ACM Queue 1:58-65.

Digital Library

[37]

Nebel B, Koehler J (1995) Plan reuse versus plan generation: a theoretical and empirical analysis. Artif Intell 76:427-454.

Digital Library

[38]

Pryor L, Collins G (1996) Planning for contingencies: a decision-based approach. J Artif Intell Res 4:287-339.

Digital Library

[39]

Refanidis I, Vlahavas I (2001) The GRT planning system: backward heuristic construction in forward state-space planning. J Artif Intell Res 15:115-161.

Digital Library

[40]

Sanchez J, Mali AD (2003) S-MEP: a planner for numeric goals. In: Proceedings of IEEE ICTAI, pp 274-283.

[41]

Sapena O, Onainda E, Mellado M, Correcher C, Vendrell E (2004) Reactive planning simulation in dynamic environments with VirtualRobot. In: Proceedings of IEA-AIE. Lecture notes in computer science, vol 3029, pp 699-707.

[42]

Schoppers MJ (1987) Universal plans for reactive robots in unpredictable domains. In: Proceedings of the 10th international joint conference on artificial intelligence (IJCAI), pp 1039-1046.

[43]

Sebastia L, Onaindia E, Marzal E (2001) STeLLa: an optimal sequential and parallel planner. In: Proceedings of EPIA. Lecture notes in artificial intelligence. Springer, Berlin, pp 409-416.

[44]

Simmons R et al (2003) GRACE: an autonomous robot for the AAAI robot challenge. AAAI Mag 24:51-72.

Digital Library

[45]

Smith DE, Weld DS (1998) Conformant graphplan. In: Proceedings of the national conference on artificial intelligence, pp 889- 896.

[46]

Thiebaux S, Hertzberg J, Shoaff W, Schneider M (1995) A stochastic model of actions and plans for anytime planning under uncertainty. Int J Intell Syst 10:155-183.

[47]

Washington R (1995) Incremental planning for truly integrated planning and reaction. Proc SCAI 28:305-316.

[48]

Weld DS (1994) An introduction to least commitment planning. AI Mag 15.

[49]

Wilkins DE (1988) Practical planning: extending the classical AI planning paradigm. Kaufmann, Los Altos.

[50]

Yang Q (1997) Intelligent planning. A decomposition and abstraction based approach. Springer, Berlin.

[51]

Zilberstein S (1996) Using anytime algorithms in intelligent systems. AI Mag 17:73-83.

Digital Library

Cited By

Babli MSapena ÓOnaindia E(2023)Plan commitmentEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106275123:PAOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106275
Nikitin NVychuzhanin PHvatov ADeeva IKalyuzhnaya AKovalchuk SAuger AStützle T(2019)Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibrationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326876(1583-1591)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326876
Barakat LMiles SLuck M(2014)Efficient adaptive QoS-based service selectionService Oriented Computing and Applications10.1007/s11761-013-0149-z8:4(261-276)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/s11761-013-0149-z
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Index Terms

Planning in highly dynamic environments: an anytime approach for planning under time constraints
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
2. Theory of computation

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cover image Applied Intelligence

Applied Intelligence Volume 29, Issue 1

August 2008

109 pages

ISSN:0924-669X

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Copyright © Copyright © 2008 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2008

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Cited By

Babli MSapena ÓOnaindia E(2023)Plan commitmentEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106275123:PAOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106275
Nikitin NVychuzhanin PHvatov ADeeva IKalyuzhnaya AKovalchuk SAuger AStützle T(2019)Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibrationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326876(1583-1591)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326876
Barakat LMiles SLuck M(2014)Efficient adaptive QoS-based service selectionService Oriented Computing and Applications10.1007/s11761-013-0149-z8:4(261-276)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/s11761-013-0149-z
Torreño AOnaindia ESapena Ó(2014)FMAPApplied Intelligence10.1007/s10489-014-0540-241:2(606-626)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s10489-014-0540-2
Sánchez-Garzón IGonzález-Ferrer AFernández-Olivares J(2014)A knowledge-based architecture for the management of patient-focused care pathwaysApplied Intelligence10.1007/s10489-013-0466-040:3(497-524)Online publication date: 1-Apr-2014
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Fenelon VSantos PDee HCozman F(2013)Reasoning about shadows in a mobile robot environmentApplied Intelligence10.1007/s10489-012-0385-538:4(553-565)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1007/s10489-012-0385-5
Wang ZWang HQi CWang J(2013)A resource enhanced HTN planning approach for emergency decision-makingApplied Intelligence10.1007/s10489-012-0367-738:2(226-238)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1007/s10489-012-0367-7
Della Penna GMagazzeni DMercorio F(2012)A universal planning system for hybrid domainsApplied Intelligence10.1007/s10489-011-0306-z36:4(932-959)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1007/s10489-011-0306-z

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