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Planning in Artificial Intelligence

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A Guided Tour of Artificial Intelligence Research

Abstract

In this chapter, we propose a non-exhaustive review of past works of the AI community on classical planning and planning under uncertainty. We first present the classical propositional STRIPS planning language. Its extensions, based on the problem description language PDDL have become a standard in the community. We briefly deal with the structural analysis of planning problems, which has initiated the development of efficient planning algorithms and associated planners. Then, we describe the Markov Decision Processes framework (MDP), initially proposed in the Operations Research community before the AI community adopted it as a framework for planning under uncertainty. Eventually, we will describe innovative (approximate or exact) MDP solution algorithms as well as recent progresses in AI in terms of knowledge representation (logics, Bayesian networks) which have been used to increase the power of expression of the MDP framework.

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Notes

  1. 1.

    International Planning Competition: http://ipc.icaps-conference.org.

References

  • Akström KJ (1965) Optimal control of Markov decision processes with incomplete state estimation. J Math Anal Appl 10:174–205

    Article  MathSciNet  Google Scholar 

  • Bacchus F (2001) The 2000 AI planning systems competition. AI Mag 22(3):47–56

    Google Scholar 

  • Bäckström C, Nebel B (1995) Complexity results for SAS\(+\) planning. Comput Intell 11(4):625–655

    Article  MathSciNet  Google Scholar 

  • Bahar RI, Frohm EA, Gaona CM, Hachtel GD, Macii E, Pardo A, Somenzi F (1997) Algebraic decision diagrams and their applications. Form Methods Syst Des 10(2–3):171–206

    Article  Google Scholar 

  • Bellman RE (1957) Dynamic programming. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Ben Amor N, El Khalfi Z, Fargier H, Sabbadin R (2018) Lexicographic refinements in possibilistic decision trees and finite-horizon markov decision processes. Fuzzy Sets Syst (In Press)

    Google Scholar 

  • Bertsekas DP (1987) Dynamic programming: deterministic and stochastic models. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Beynier A, Charpillet F, Szer D, Mouaddib A (2010) DEC-MDP/POMDP. Markov decision processes and artificial intelligence. Wiley, New York, pp 321–359

    Google Scholar 

  • Bibai J, Savéant P, Schoenauer M, Vidal V (2010) An evolutionary metaheuristic based on state decomposition for domain-independent satisficing planning. Proceedings of ICAPS, pp 18–25

    Google Scholar 

  • Blum AL, Furst ML (1997) Fast planning through planning graph analysis. Artif Intell 90(1–2):279–298

    MATH  Google Scholar 

  • Blythe J (1999) An overview of planning under uncertainty. AI Mag 20(2):37–54

    MathSciNet  Google Scholar 

  • Bonet B, Geffner H (1999) Planning as heuristic search: New results. In: Proceedings of ECP, pp 359–371

    Google Scholar 

  • Bonet B, Geffner H (2001) Planning as heuristic search. Artif Intell 129(1–2):5–33

    Article  MathSciNet  MATH  Google Scholar 

  • Bonet B, Geffner H (2005) mGPT: a probabilistic planner based on heuristic search. J Artif Intell Res 24:933–944

    Article  MATH  Google Scholar 

  • Bonet B, Loerincs G, Geffner H (1997) A robust and fast action selection mechanism for planning. In: Proceedings of AAAI, pp 714–719

    Google Scholar 

  • Botea A, Enzenberger M, Müller M, Schaeffer J (2005) Macro-FF: improving AI planning with automatically learned macro-operators. J Artif Intell Res 24:581–621

    Article  MATH  Google Scholar 

  • Boutilier C, Brafman RI, Geib CW (1998) Structured reachability analysis for Markov decision processes. In: Proceedings of UAI, pp 24–32

    Google Scholar 

  • Boutilier C, Dearden R, Goldszmidt M (2000) Stochastic dynamic programming with factored representations. Artif Intell 121(1–2):49–107

    Article  MathSciNet  MATH  Google Scholar 

  • Bryant RE (1986) Graph-based algorithms for boolean function manipulation. IEEE Trans Comput 35(8):677–691

    Article  MATH  Google Scholar 

  • Buffet O, Aberdeen D (2007) FF \(+\) FPG: guiding a policy-gradient planner. In: Proceedings of ICAPS, pp 42–48

    Google Scholar 

  • Buffet O, Aberdeen D (2009) The factored policy-gradient planner. Artif Intell 173(5–6):722–747

    Article  MathSciNet  MATH  Google Scholar 

  • Buffet O, Sigaud O (eds) (2008a). Processus décisionnels de Markov en intelligence artificielle - vol. 1. Traité IC2 - Informatique et systèmes d’information. Hermes - Lavoisier, Cachan

    Google Scholar 

  • Buffet O, Sigaud O (eds) (2008b) Processus décisionnels de Markov en intelligence artificielle - vol. 2. Traité IC2 - Informatique et systèmes d’information. Hermes - Lavoisier, Cachan

    Google Scholar 

  • Burns E, Lemons S, Zhou R, Ruml W (2009) Best-first heuristic search for multi-core machines. In: Proceedings of IJCAI, pp 449–455

    Google Scholar 

  • Bylander T (1994) The computational complexity of propositional STRIPS planning. Artif Intell 69(1–2):165–204

    Article  MathSciNet  MATH  Google Scholar 

  • Cai D, Hoffmann J, Helmert M (2009) Enhancing the context-enhanced additive heuristic with precedence constraints. In Proceedings of ICAPS

    Google Scholar 

  • Canu A, Mouaddib A (2011) Dynamic local interaction model: framework and algorithms. In: Proceedings of AAMAS, workshop of multi-agent sequential decision making in uncertain multi-agent domains (MSDM)

    Google Scholar 

  • Chen Y, Hsu C, Wah B (2006) Temporal planning using subgoal partitioning and resolution in SGPlan. Artif Intell 26:323–369

    MATH  Google Scholar 

  • Chien S, Rabideau G, Knight R, Sherwood R, Engelhardt B, Mutz D, Estlin T, Smith B, Fisher F, Barrett T, Stebbins G, Tran D (2000) ASPEN - automating space mission operations using automated planning and scheduling. In: Proceedings of the international conference on space operations (SpaceOps)

    Google Scholar 

  • Coles A, Smith KA (2007) Marvin: a heuristic search planner with online macro-action learning. J Artif Intell Res 28:119–156

    Article  MATH  Google Scholar 

  • Coles A, Fox M, Long D, Smith A (2008) Planning with problems requiring temporal coordination. In: Proceedings of AAAI, pp 892–897

    Google Scholar 

  • Coles A, Coles A, Fox M, Long D (2009) Temporal planning in domains with linear processes. In: Proceedings of IJCAI, pp 1671–1676

    Google Scholar 

  • Cushing W, Kambhampati S, Mausam, Weld, DS (2007a) When is temporal planning really temporal? In: Proceedings of IJCAI, pp 1852–1859

    Google Scholar 

  • Cushing W, Weld DS, Kambhampati S, Mausam, Talamadupula, K. (2007b) Planning with graded non deterministic actions: a possibilistic approach. In: Proceedings of ICAPS, pp 105–112

    Google Scholar 

  • Da Costa PC, Garcia F, Lang J, Martin-Clouaire R (1997) Planning with graded non deterministic actions: a possibilistic approach. Int J Intell Syst 12:935–962

    Article  Google Scholar 

  • Dean T, Kanazawa K (1990) A model for reasoning about persistence and causation. Comput Intell 5(3):142–150

    Google Scholar 

  • Do M, Kambhampati S (2003) Sapa: a multi-objective metric temporal planner. J Artif Intell Res 20:155–194

    Article  MATH  Google Scholar 

  • Do MB, Kambhampati S (2001) Planning as constraint satisfaction: solving the planning graph by compiling it into CSP. Artif Intell 132(2):151–182

    Article  MathSciNet  MATH  Google Scholar 

  • Durfee EH (1999) Distributed problem solving and planning. Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, pp 121–164

    Google Scholar 

  • Edelkamp S, Kissmann P (2008) GAMER: bridging planning and general game playing with symbolic search. In: Proceedings of IPC

    Google Scholar 

  • Fabre E, Jezequel L, Haslum P, Thiébaux S (2010) Cost-optimal factored planning: promises and pitfalls. In: Proceedings of ICAPS, pp 65–72

    Google Scholar 

  • Feng Z, Hansen EA (2002) Symbolic heuristic search for factored Markov decision processes. In: Proceedings of AAAI, pp 455–460

    Google Scholar 

  • Feng Z, Hansen EA, Zilberstein S (2003) Symbolic generalization for on-line planning. In: Proceedings of UAI, pp 209–216

    Google Scholar 

  • Fikes R, Nilsson NJ (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3–4):189–208

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Fox M, Long D (2006) Modelling mixed discrete-continuous domains for planning. J Artif Intell Res 27:235–297

    Article  MATH  Google Scholar 

  • Garcia L, Sabbadin R (2008) Complexity results and algorithms for possibilistic influence diagrams. Artif Intell 172(8–9):1018–1044

    Article  MATH  Google Scholar 

  • Gazen B, Knoblock C (1997) Combining the expressiveness of UCPOP with the efficiency of Graphplan. In: Proceedings of ECP, pp 221–233

    Google Scholar 

  • Geffner H (2000) Functional STRIPS: A more flexible language for planning and problem solving. In: Minker J (ed) Logic-based artificial intelligence. Kluwer, Alphen aan den Rijn, pp 187–209

    Chapter  Google Scholar 

  • Gerevini A, Long D (2005) Plan constraints and preferences in PDDL3. Technical report RT 2005–08-47, Department of Electronics for Automation, University of Brescia, Italy

    Google Scholar 

  • Gerevini A, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the 5th international planning competition: PDDL3 and experimental evaluation of the planners. Artif Intell 173(5–6):619–668

    Article  MATH  Google Scholar 

  • Ghallab M, Laruelle H (1994) Representation and control in IxTeT, a temporal planner. In: Proceedings of AIPS, pp 61–67

    Google Scholar 

  • Ghallab M, Nau D, Traverso P (2004) Automated planning: theory and practice. Morgan Kaufmann, San Francisco

    MATH  Google Scholar 

  • Ghavamzadeh M, Mannor S, Pineau J, Tamar A (2015) Bayesian reinforcement learning: a survey. Found Trends Mach Learn 8(5–6):359–483

    Article  MATH  Google Scholar 

  • Givan R, Dean T, Greig M (2003) Equivalence notions and model minimization in Markov decision processes. Artif Intell 147(1–2):163–223

    Article  MathSciNet  MATH  Google Scholar 

  • Grandcolas S, Pain-Barre C (2007) Filtering, decomposition and search space reduction for optimal sequential planning. In: Proceedings of AAAI, pp 993–998

    Google Scholar 

  • Hart P, Nilsson N, Raphael B (1968) A formal basis for the heuristic determination of minimum-cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107

    Article  Google Scholar 

  • Haslum P (2006) Improving heuristics through relaxed search - an analysis of TP4 and HSP*a in the 2004 planning competition. J Artif Intell Res 25:233–267

    Article  MATH  Google Scholar 

  • Haslum P, Geffner H (2000) Admissible heuristics for optimal planning. In: Proceedings of AIPS, pp 70–82

    Google Scholar 

  • Haslum P, Geffner H (2001) Heuristic planning with time and resources. In: Proceedings of ECP, pp 121–132

    Google Scholar 

  • Helmert M (2003) Complexity results for standard benchmark domains in planning. Artif Intell 143(2):219–262

    Article  MathSciNet  MATH  Google Scholar 

  • Helmert M (2004) A planning heuristic based on causal graph analysis. In: Proceedings of ICAPS, pp 161–170

    Google Scholar 

  • Helmert M (2006) The fast downward planning system. J Artif Intell Res 26:191–246

    Article  MATH  Google Scholar 

  • Helmert M (2008) Understanding planning tasks. Springer, Berlin

    MATH  Google Scholar 

  • Helmert M, Domshlak C (2009) Landmarks, critical paths and abstractions: what’s the difference anyway? In: Proceedings of ICAPS, pp 162–169

    Google Scholar 

  • Helmert M, Geffner H (2008) Unifying the causal graph and additive heuristics. In: Proceedings of ICAPS, pp 140–147

    Google Scholar 

  • Helmert M, Haslum P, Hoffmann J (2007) Flexible abstraction heuristics for optimal sequential planning. In: Proceedings of ICAPS, pp 176–183

    Google Scholar 

  • Helmert M, Do MB, Refanidis I (2008a) IPC-2008, deterministic part: changes in PDDL 3.1. http://ipc08.icaps-conference.org/PddlExtension

  • Helmert M, Do MB, Refanidis I (2008b) IPC-2008, deterministic part: results. http://ipc08.icaps-conference.org/Results

  • Hickmott SL, Rintanen J, Thiébaux S, White LB (2007) Planning via petri net unfolding. In: Proceedings of IJCAI, pp 1904–1911

    Google Scholar 

  • Hoey J, St-Aubin R, Hu AJ, Boutilier C (1999) SPUDD: Stochastic planning using decision diagrams. In: Proceedings of UAI, pp 279–288

    Google Scholar 

  • Hoffmann J (2002) Extending FF to numerical state variables. In: Proceedings of ECAI, pp 571–575

    Google Scholar 

  • Hoffmann J, Edelkamp S (2004) PDDL2. 2: the language for the classical part of IPC-4. In: Proceedings of IPC

    Google Scholar 

  • Hoffmann J, Edelkamp S (2005) The deterministic part of IPC-4: an overview. J Artif Intell Res 24:519–579

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Hoffmann J, Porteous J, Sebastia L (2004) Ordered landmarks in planning. J Artif Intell Res 22:215–278

    Article  MathSciNet  MATH  Google Scholar 

  • Huang R, Chen Y, Zhang W (2010) A novel transition based encoding scheme for planning as satisfiability. In: Proceedings of AAAI

    Google Scholar 

  • Jensen FV (2001) Bayesian networks and decision graphs. Springer, Berlin

    Book  MATH  Google Scholar 

  • Joshi S, Kersting K, Khardon R (2010) Self-taught decision theoretic planning with first order decision diagrams. In: Proceedings of ICAPS, pp 89–96

    Google Scholar 

  • Kaelbling LP, Littman ML, Cassandra AR (1998) Planning and acting in partially observable domains. Artif Intell 101(1–2):99–134

    Article  MathSciNet  MATH  Google Scholar 

  • Karpas E, Domshlak C (2009) Cost-optimal planning with landmarks. In: Proceedings of IJCAI, pp 1728–1733

    Google Scholar 

  • Kautz H, Selman B (1999) Unifying SAT-based and graph-based planning. In: Proceedings of IJCAI, pp 318–325

    Google Scholar 

  • Kautz H, McAllester D, Selman B (1996) Encoding plans in propositional logic. In: Proceedings of KR, pp 374–384

    Google Scholar 

  • Keller T, Eyerich P (2012) Prost: probabilistic planning based on uct. In: Proceedings of ICAPS

    Google Scholar 

  • Keller T, Helmert M (2013) Trial-based heuristic tree search for finite horizon mdps. In: Proceedings of ICAPS

    Google Scholar 

  • Keyder E, Geffner H (2008) Heuristics for planning with action costs revisited. In: Proceedings of ECAI, pp 588–592

    Google Scholar 

  • Keyder E, Richter S, Helmert M (2010) Sound and complete landmarks for and/or graphs. In: Proceedings of ECAI, pp 335–340

    Google Scholar 

  • Kishimoto A, Fukunaga AS, Botea A (2009) Scalable, parallel best-first search for optimal sequential planning. In: Proceedings of ICAPS, pp 10–17

    Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • Koehler J, Nebel B, Hoffmann J, Dimopoulos Y (1997) Extending planning graphs to an ADL subset. In: Proceedings of ECP, pp 273–285

    Google Scholar 

  • Kolobov A, Mausam M, Weld DS (2009) ReTrASE: integrating paradigms for approximate probabilistic planning. In: Proceedings of IJCAI, pp 1746–1753

    Google Scholar 

  • Kolobov A, Mausam, Weld DS (2010a) Classical planning in MDP heuristics: with a little help from generalization. In: Proceedings of ICAPS, pp 97–104

    Google Scholar 

  • Kolobov A, Mausam, Weld DS (2010b) Sixthsense: fast and reliable recognition of dead ends in MDPs. In: Proceedings of AAAI

    Google Scholar 

  • Kolobov A, Mausam Weld DS, Geffner H (2011) Heuristic search for generalized stochastic shortest path mdps. In: Proceedings of ICAPS

    Google Scholar 

  • Kolobov A, Dai P, Mausam M, Weld DS (2012a) Reverse iterative deepening for finite-horizon MDPs with large branching factors. In: Proceedings of ICAPS

    Google Scholar 

  • Kolobov A, Mausam, Weld DS (2012b) A theory of goal-oriented MDPs with dead ends. In: Proceedings of UAI, pp 438–447

    Google Scholar 

  • Korf R (1985) Depth-first iterative-deepening: an optimal admissible tree search. Artif Intell 27(1):97–109

    Article  MathSciNet  MATH  Google Scholar 

  • Kumar PR, Varaiya PP (1986) Stochastic systems: estimation, identification and adaptive control. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Kuter U, Nau DS (2005) Using domain-configurable search control for probabilistic planning. In: Proceedings of AAAI, pp 1169–1174

    Google Scholar 

  • Laborie P (2003) Algorithms for propagating resource constraints in AI planning and scheduling. Artif Intell 143(2):151–188

    Article  MathSciNet  MATH  Google Scholar 

  • Laborie P, Ghallab M (1995) Planning with sharable resources constraints. In: Proceedings of IJCAI, pp 1643–1649

    Google Scholar 

  • Littman ML (1994) Memoryless policies: theoretical limitations and practical results. In: Proceedings of ICSAB, pp 238–245

    Google Scholar 

  • Long D, Fox M (1999) The efficient implementation of the plan-graph. J Artif Intell Res 10:85–115

    MATH  Google Scholar 

  • Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res 20:1–59

    Article  MATH  Google Scholar 

  • Maris F, Régnier P (2008) TLP-GP: solving temporally-expressive planning problems. In: Proceedings of TIME, pp 137–144

    Google Scholar 

  • Maris F, Régnier P, Vidal V (2008) Planification par satisfaction de bases de clauses. In: Saïs L (ed) Problème SAT : défis et challenges (Chap. 11). Hermes, pp 289–309

    Google Scholar 

  • McDermott D (1996) A heuristic estimator for means-ends analysis in planning. In: Proceedings of AIPS, pp 142–149

    Google Scholar 

  • McDermott D (2000) The 1998 AI planning systems competition. AI Mag 21(2):35–56

    Google Scholar 

  • McDermott D, Ghallab M, Howe A, Knoblock C, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL - The Planning Domain Definition Language. Technical report CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control, New Haven, CI, USA

    Google Scholar 

  • Meseguer P, Rossi F, Schiex T (2006) Soft constraints. In: Rossi F, van Beek P, Walsh T (eds) Handbook of constraint programming (Chap. 9). Elsevier, Amsterdam, pp 281–328

    Google Scholar 

  • Moore AW, Atkeson CG (1993) Prioritized sweeping: reinforcement learning with less data and less real time. Mach Learn 13:103–130

    Google Scholar 

  • Mouaddib AI, Pralet C, Sabbadin R, Weng P (2008) Processus décisionnels de Markov et critères non classiques. In: Buffet O, Sigaud O (eds) Processus décisionnels de Markov en intelligence artificielle - vol 1 (Chap. 5). Hermes - Lavoisier, Cachan

    Google Scholar 

  • Newell A, Simon H (1963) GPS: a program that simulates human thought. In: Feigenbaum E, Feldman J (eds) Computers and thought. McGraw Hill, New-York, pp 279–293

    Google Scholar 

  • Nguyen X, Kambhampati S (2001) Reviving partial order planning. In: Proceedings of IJCAI, pp 459–466

    Google Scholar 

  • Pearl J (1983) Heuristics. Addison Wesley, Reading

    Google Scholar 

  • Pednault EPD (1989) ADL: exploring the middle ground between STRIPS and the situation calculus. In: Proceedings of IJCAI, pp 324–332

    Google Scholar 

  • Penberthy J, Weld D (1992) UCPOP: a sound, complete, partial order planner for ADL. In: Proceeidngs of KR, pp 103–114

    Google Scholar 

  • Peng J, Williams RJ (1993) Efficient learning and planning within the dyna framework. Adapt Behav 1(4):437–454

    Article  Google Scholar 

  • Pineau J, Gordon G, Thrun S (2003) Point-based value iteration: an anytime algorithm for POMDPs. In: Proceedings of IJCAI, pp 1025–1032

    Google Scholar 

  • Pohl I (1970) Heuristic search viewed as path finding in a graph. Artif Intell 1(3):193–204

    Article  MathSciNet  MATH  Google Scholar 

  • Pralet C, Verfaillie G (2010) Réseaux de contraintes sur des chronogrammes pour la planification et l’ordonnancement. Revue d’Intelligence Artificielle 24(4):485–504

    Article  Google Scholar 

  • Puterman ML (1994) Markov decision processes: discrete stochastic dynamic programming. Wiley, New York

    Book  MATH  Google Scholar 

  • Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39:127–177

    Article  MATH  Google Scholar 

  • Richter S, Helmert M, Westphal M (2008) Landmarks revisited. In: Proceedings of AAAI, pp 975–982

    Google Scholar 

  • Rintanen J (2007) Complexity of concurrent temporal planning. In: Proceedings of ICAPS, pp 280–287

    Google Scholar 

  • Rintanen J (2010) Heuristics for planning with SAT. In: Proceedings of CP, pp 414–428

    Google Scholar 

  • Sabbadin R (2001) Possibilistic Markov decision processes. Eng Appl Artif Intell 14:287–300

    Article  Google Scholar 

  • Sabbadin R, Fargier H, Lang J (1998) Towards qualitative approaches to multi-stage decision making. Int J Approx Reason 19(3–4):441–471

    Article  MathSciNet  MATH  Google Scholar 

  • Sanner, S. (2010). Relational dynamic influence diagram language (RDDL): language description. http://users.cecs.anu.edu.au/~ssanner/IPPC_2011/RDDL.pdf

  • Shoham Y, Leyton-Brown K (2009) Multiagent systems: algorithmic, game-theoretic, and logical foundations. Cambridge University Press, New York

    MATH  Google Scholar 

  • Smith DE, Weld DS (1999) Temporal planning with mutual exclusion reasoning. In: Proceedings of IJCAI, pp 326–337

    Google Scholar 

  • Smith T, Simmons R (2005) Point-based POMDP algorithms: improved analysis and implementation. In: Proceedings of UAI, pp 542–547

    Google Scholar 

  • Sondik EJ (1978) The optimal control of partially observable Markov processes over the infinite horizon: discounted costs. Oper Res 26(2):282–304

    Article  MathSciNet  MATH  Google Scholar 

  • St-Aubin R, Hoey J, Boutilier C (2000) APRICODD: approximate policy construction using decision diagrams. In: Proceedings of NIPS, pp 1089–1095

    Google Scholar 

  • Sutton R (1988) Learning to predict by the method of temporal differences. Mach Learn 3(1):9–44

    Google Scholar 

  • Sutton R (1991) Planning by incremental dynamic programming. In: Proceedings of IWML, pp 353–357

    Google Scholar 

  • Teichteil-Königsbuch F (2012) Stochastic safest and shortest path problems. In: Proceedings of AAAI

    Google Scholar 

  • Teichteil-Königsbuch F, Kuter U, Infantes G (2010) Incremental plan aggregation for generating policies in MDPs. In: Proceedings of AAMAS, pp 1231–1238

    Google Scholar 

  • Teichteil-Königsbuch F, Vidal V, Infantes G (2011) Extending classical planning heuristics to probabilistic planning with dead-ends. In: Proceedings of AAAI

    Google Scholar 

  • Thiébaux S, Hoffmann J, Nebel B (2003) In defense of PDDL axioms. In: Proceedings of IJCAI, pp 961–968

    Google Scholar 

  • Trevizan FW, Veloso MM (2014) Depth-based short-sighted stochastic shortest path problems. Artif Intell 216:179–205

    Article  MathSciNet  MATH  Google Scholar 

  • Trevizan FW, Thiébaux S, Santana PH, Williams BC (2016) Heuristic search in dual space for constrained stochastic shortest path problems. In: Proceedings of ICAPS, pp 326–334

    Google Scholar 

  • Valenzano R, Sturtevant N, Schaeffer J, Buro K, Kishimoto A (2010) Simultaneously searching with multiple settings: an alternative to parameter tuning for suboptimal single-agent search algorithms. In: Proceedings of ICAPS, pp 177–184

    Google Scholar 

  • Verfaillie G, Pralet C, Lemaître M (2010) How to model planning and scheduling problems using constraint networks on timelines. Knowl Eng Rev 25(3):319–336

    Article  Google Scholar 

  • Vidal V (2001) Recherche dans les graphes de planification, satisfiabilité et stratégies de moindre engagement. Les systèmes LCGP et LCDPP. PhD thesis, IRIT, Université Paul Sabatier, Toulouse, France

    Google Scholar 

  • Vidal V (2004) A lookahead strategy for heuristic search planning. In: Proceedings of ICAPS, pp 150–160

    Google Scholar 

  • Vidal V, Geffner H (2005) Solving simple planning problems with more inference and no search. In: Proceedings of CP, pp 682–696

    Google Scholar 

  • Vidal V, Geffner H (2006) Branching and pruning: an optimal temporal POCL planner based on constraint programming. Artif Intell 170(3):298–335

    Article  MathSciNet  MATH  Google Scholar 

  • Vidal V, Bordeaux L, Hamadi Y (2010) Adaptive K-parallel best-first search: a simple but efficient algorithm for multi-core domain-independent planning. In: Proceedings of SOCS

    Google Scholar 

  • Vlassis N (2009) Synthesis lectures on artificial intelligence and machine learning. Morgan & Claypool Publishers, San Rafael

    Google Scholar 

  • Watkins CJ (1989) Learning from Delayed Rewards. PhD thesis, King’s College, Cambridge, UK

    Google Scholar 

  • Watkins CJ, Dayan P (1992) Q-learning. Mach Learn 3(8):279–292

    MATH  Google Scholar 

  • Weld DS (1994) An introduction to least commitment planning. AI Mag 15(4):27–61

    Google Scholar 

  • Yoon SW, Fern A, Givan R (2007) FF-replan: a baseline for probabilistic planning. In: Proceedings of ICAPS, pp 352–359

    Google Scholar 

  • Yoon SW, Fern A, Givan R, Kambhampati S (2008) Probabilistic planning via determinization in hindsight. In: Proceedings of AAAI, pp 1010–1016

    Google Scholar 

  • Yoon SW, Ruml W, Benton J, Do MB (2010) Improving determinization in hindsight for on-line probabilistic planning. In: Proceedings of ICAPS, pp 209–217

    Google Scholar 

  • Younes HLS, Simmons RG (2003) VHPOP: versatile heuristic partial order planner. J Artif Intell Res 20:405–430

    Article  MATH  Google Scholar 

  • Younes HLS, Littman ML, Weissman D, Asmuth J (2005) The first probabilistic track of the international planning competition. J Artif Intell Res 24:851–887

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. MIAT, UR 875, INRA, 31320, Castanet-Tolosan, France

    Régis Sabbadin

  2. AIRBUS, Toulouse, France

    Florent Teichteil-Königsbuch

  3. ONERA, Toulouse, France

    Vincent Vidal

Authors
  1. Régis Sabbadin
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  2. Florent Teichteil-Königsbuch
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  3. Vincent Vidal
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Correspondence to Régis Sabbadin .

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Editors and Affiliations

  1. CRIL-CNRS, Université d’Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

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Sabbadin, R., Teichteil-Königsbuch, F., Vidal, V. (2020). Planning in Artificial Intelligence. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06167-8_10

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