AI Reasoning Methods for Robotics

  • Michael BeetzEmail author
  • Raja Chatila
  • Joachim Hertzberg
  • Federico Pecora
Part of the Springer Handbooks book series (SHB)


Artificial intelligence (AI ) reasoning technology involving, e. g., inference, planning, and learning, has a track record with a healthy number of successful applications. So can it be used as a toolbox of methods for autonomous mobile robots? Not necessarily, as reasoning on a mobile robot about its dynamic, partially known environment may differ substantially from that in knowledge-based pure software systems, where most of the named successes have been registered. Moreover, recent knowledge about the robot’s environment cannot be given a priori, but needs to be updated from sensor data, involving challenging problems of symbol grounding and knowledge base change.

This chapter sketches the main robotics-relevant topics of symbol-based AI reasoning. Basic methods of knowledge representation and inference are described in general, covering both logic- and probability-based approaches. The chapter first gives a motivation by example, to what extent symbolic reasoning has the potential of helping robots perform in the first place. Then (Sect. 14.2), we sketch the landscape of representation languages available for the endeavor. After that (Sect. 14.3), we present approaches and results for several types of practical, robotics-related reasoning tasks, with an emphasis on temporal and spatial reasoning. Plan-based robot control is described in some more detail in Sect. 14.4. Section 14.5 concludes.


Bayesian Network Description Logic Constraint Satisfaction Problem Plan Execution Temporal Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Association for the Advancement of Artificial Intelligence


artificial intelligence


Bayesian network


cardinal direction calculus


constraint satisfaction problem


dynamic Bayesian network




description logic


Davis–Putnam algorithm


European Conference on Artificial Intelligence


externally connected


fast forward


first-order predicate logic


hierarchical task network


interval algebra


International Conference on Automated Planning and Scheduling


International Joint Conference on Artificial Intelligence


international AI planning competition


knowledge representation


linear temporal logic


Markov decision process


nontangential proper part


ontology based unified robot knowledge


web ontology language


point algebra


policy iteration


partially observable Markov decision process


partially overlapping


partial-order planning


probabilistic roadmap


rectangle algebra


region connection calculus


International Conference on Theory and Applications of Satisfiability Testing


satisfiabiliy modulo theory


simple temporal problem


temporal action logic


temporal constraint satisfaction problem


temporal logic


tangential proper part


value iteration


WWW consortium


  1. 14.1
    S. Harnad: The symbol grounding problem, Physica D 42, 335–346 (1990)CrossRefGoogle Scholar
  2. 14.2
    S. Coradeschi, A. Saffiotti: An introduction to the anchoring problem, Robotics Auton. Syst. 43(2/3), 85–96 (2003)CrossRefGoogle Scholar
  3. 14.3
    F. Baader, D. Calvanese, D. McGuinness, D. Nardi, P. Patel-Schneider (Eds.): The Description Logic Handbook (Cambridge Univ. Press, Cambridge 2003)zbMATHGoogle Scholar
  4. 14.4
    S.J. Russell, P. Norvig: Artificial Intelligence: A Modern Approach, 3rd edn. (Pearson Education, Upper Saddle River 2010)zbMATHGoogle Scholar
  5. 14.5
    R.J. Brachman, H.J. Levesque: Knowledge Representation and Reasoning (Morgan Kaufmann, San Francisco 2004)zbMATHGoogle Scholar
  6. 14.6
    W.O. van Quine: Methods of Logic, 4th edn. (Harvard Univ. Press, Cambridge 1982)zbMATHGoogle Scholar
  7. 14.7
    Z. Manna, R. Waldinger: The Deductive Foundations of Computer Programming: A One-Volume Version of ‘‘The Logical Basis for Computer Programming’’ (Addison-Wesley, Reading 1993)zbMATHGoogle Scholar
  8. 14.8
    W. Hodges: Elementary predicate logic. In: Handbook of Philosophical Logic, Vol. 1, ed. by D. Gabbay, F. Guenthner (D. Reidel, Dordrecht 1983)Google Scholar
  9. 14.9
    A. Robinson, A. Voronkov (Eds.): Handbook of Automated Reasoning (Elsevier, Amsterdam 2001)zbMATHGoogle Scholar
  10. 14.10
    M. Davis, G. Logemann, D. Loveland: A machine program for theorem proving, Communications ACM 5(7), 394–397 (1962)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 14.11
    The international SAT Competitions web page:
  12. 14.12
    The Web Ontology Language OWL:
  13. 14.13
    OWL 2 Web Ontology Language Document Overview (Second Edition):
  14. 14.14
    P. Hitzler, M. Krötzsch, S. Rudolph: Foundations of Semantic Web Technologies (Chapman Hall/CRC, Boca Raton 2009)Google Scholar
  15. 14.15
    J. McCarthy, P. Hayes: Some philosophical problems from the standpoint of artificial intelligence, Mach. Intell. 4, 463–507 (1969)zbMATHGoogle Scholar
  16. 14.16
    H. Levesque, R. Reiter, Y. Lespérance, F. Lin, R. Scherl: Golog: A logic programming language for dynamic domains, J. Log. Program. 31, 59–83 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 14.17
    M. Shanahan, M. Witkowski: High-level robot control through logic, ATAL ’00: Proc. 7th Int. Workshop Intell. Agents VII. Agent Theor. Archit. Lang. (2001) pp. 104–121CrossRefGoogle Scholar
  18. 14.18
    M. Thielscher: Reasoning Robots. The Art and Science of Programming Robotic Agents (Springer, Berlin 2005)zbMATHGoogle Scholar
  19. 14.19
    P. Doherty, J. Kvarnström: TALplanner: A temporal logic based planner, AI Magazine 22(3), 95–102 (2001)zbMATHGoogle Scholar
  20. 14.20
    K.L. Chung, F. AitSahila: Elementary Probability Theory, 4th edn. (Springer, Berlin, Heidelberg 2003)CrossRefGoogle Scholar
  21. 14.21
    J. Pearl: Probabilistic Reasoning in Intelligent Systems (Morgan Kaufmann, San Mateo 1988)zbMATHGoogle Scholar
  22. 14.22
    A.R. Cassandra, L.P. Kaelbling, M.L. Littman: Acting Optimally in Partially Observable Stochastic Domains, Tech. Rep. AAAI-94 (Department of Computer Science, Brown University, Providence 1994) pp. 1023–1028zbMATHGoogle Scholar
  23. 14.23
    R.E. Bellman: Dynamic Programming (Princeton Univ. Press, Princeton 1957)zbMATHGoogle Scholar
  24. 14.24
    L.P. Kaelbling, T. Lozano-Pérez: Integrated task and motion planning in belief space, Int. J. Robotics Res. 32(9/10), 1194–1227 (2013)CrossRefGoogle Scholar
  25. 14.25
    R.E. Fikes, N.J. Nilsson: Strips: A new approach to theorem proving in problem solving, J. Artif. Intell. 2, 189–208 (1971)zbMATHCrossRefGoogle Scholar
  26. 14.26
    P.E. Hart, N.J. Nilsson, B. Raphael: A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)CrossRefGoogle Scholar
  27. 14.27
    M. Ghallab, D.S. Nau, P. Traverso: Automated Planning – Theory and Practice (Elsevier, Amsterdam 2004)zbMATHGoogle Scholar
  28. 14.28
    A. Pnueli: The temporal logic of programs, Proc. 18th Annu. Symp. Found. Comput. Sci., Providence (1977) pp. 46–57Google Scholar
  29. 14.29
    O. Kupferman, M.Y. Vardi: Model checking of safety properties, Form. Methods Syst. Des. 19(3), 291–314 (2001)zbMATHCrossRefGoogle Scholar
  30. 14.30
    E. Plaku, L.E. Kavraki, M.Y. Vardi: Hybrid systems: From verification to falsification by combining motion planning and discrete search, Form. Methods Syst. Des. 34, 157–182 (2009)zbMATHCrossRefGoogle Scholar
  31. 14.31
    A. Bhatia, M.R. Maly, L.E. Kavraki, M.Y. Vardi: Motion planning with complex goals, IEEE Robotics Autom. Mag. 18(3), 55–64 (2011)CrossRefGoogle Scholar
  32. 14.32
    M. Vilain, H. Kautz, P. van Beek: Constraint propagation algorithms for temporal reasoning: A revised report. In: Readings in Qualitative Reasoning About Physical Systems, ed. by D.S. Weld, J. de Kleer (Morgan Kaufmann, San Francisco 1990) pp. 373–381CrossRefGoogle Scholar
  33. 14.33
    J. Allen: Towards a general theory of action and time, Artif. Intell. 23(2), 123–154 (1984)zbMATHCrossRefGoogle Scholar
  34. 14.34
    E.P.K. Tsang: Foundations of Constraint Satisfaction (Academic Press, London, San Diego 1993)Google Scholar
  35. 14.35
    P. Jonsson, A. Krokhin: Complexity classification in qualitative temporal constraint reasoning, Artif. Intell. 160(1/2), 35–51 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  36. 14.36
    G. Ligozat: A new proof of tractability for ORD-horn relations, AAAI Workshop Spat. Temp. Reason., Portland (1996)Google Scholar
  37. 14.37
    U. Montanari: Networks of constraints: Fundamental properties and applications to picture processing, Inf. Sci. 7, 95–132 (1974)MathSciNetzbMATHCrossRefGoogle Scholar
  38. 14.38
    R.W. Floyd: Algorithm 97: Shortest path, Communications ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  39. 14.39
    L. Xu, B.Y. Choueiry: A new efficient algorithm for solving the simple temporal problem, Proc. 4th Int. Conf. Temp. Log., Cairns (2003)Google Scholar
  40. 14.40
    L.R. Planken, M.M. De Weerdt, R.P.J. van der Krogt: P3C: A new algorithm for the simple temporal problem, Proc. Int. Conf. Autom. Plan. Sched. (ICAPS), Sydney (2008) pp. 256–263Google Scholar
  41. 14.41
    C. McGann, F. Py, K. Rajan, J. Ryan, R. Henthorn: Adaptive control for autonomous underwater vehicles, Proc. 23rd Natl. Conf. Artif. Intell., Chicago (2008) pp. 1319–1324Google Scholar
  42. 14.42
    B.C. Williams, M.D. Ingham, S.H. Chung, P.H. Elliott: Model-based programming of intelligent embedded systems and robotic space explorers, Proceedings IEEE 91(1), 212–237 (2003)CrossRefGoogle Scholar
  43. 14.43
    T. Vidal, H. Fargier: Handling contingency in temporal constraint networks: From consistency to controllabilities, J. Exp. Theor. Artif. Intell. 11, 23–45 (1999)zbMATHCrossRefGoogle Scholar
  44. 14.44
    P. Doherty, J. Kvarnström, F. Heintz: A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems, J. Auton. Agents Multi-Agent Syst. 2(2), 332–377 (2009)CrossRefGoogle Scholar
  45. 14.45
    F. Pecora, M. Cirillo, F. Dell’Osa, J. Ullberg, A. Saffiotti: A constraint-based approach for proactive, context-aware human support, J. Ambient Intell. Smart Environ. 4(4), 347–367 (2012)Google Scholar
  46. 14.46
    R. Dechter: Constraint Processing, The Morgan Kaufmann Series in Artificial Intelligence (Morgan Kaufmann, San Francisco 2003) pp. 155–165Google Scholar
  47. 14.47
    A. Loutfi, S. Coradeschi, M. Daoutis, J. Melchert: Using knowledge representation for perceptual anchoring in a robotic system, Int. J. Artif. Intell. Tools 17(5), 925–944 (2008)CrossRefGoogle Scholar
  48. 14.48
    O. Colliot, O. Camara, I. Bloch: Integration of fuzzy spatial relations in deformable models – Application to brain MRI segmentation, Pattern Recogn. 39(8), 1401–1414 (2006)CrossRefGoogle Scholar
  49. 14.49
    X. Wang, J.M. Keller, P. Gader: Using spatial relationships as features in object recognition, Annu. Meet. North Am. Fuzzy Inf. Proces. Soc., Syracuse (1997)Google Scholar
  50. 14.50
    D.A. Randell, Z. Cui, A.G. Cohn: A Spatial Logic based on Regions and Connection, Proc. Int. Conf. Princ. Knowl. Represent. Reason., Cambridge (1992)Google Scholar
  51. 14.51
    S. Skiadopoulos, M. Koubarakis: Composing cardinal direction relations, Artif. Intell. 152(2), 143–171 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  52. 14.52
    J. Renz, B. Nebel: Qualitative spatial reasoning using constraint calculi. In: Handbook of Spatial Logics, ed. by M. Aiello, I. Pratt-Hartmann, J.F.A.K. van Benthem (Springer, Berlin, Heidelberg 2007) pp. 161–215CrossRefGoogle Scholar
  53. 14.53
    T. Drakengren, P. Jonsson: A complete classification of tractability in Allen’s algebra relative to subsets of basic relations, Artif. Intell. 106(2), 205–219 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  54. 14.54
    A.G. Cohn, J. Renz, M. Sridhar: Thinking inside the box: A comprehensive spatial representation for video analysis, Proc. 13th Int. Conf. Princ. Knowl. Represent. Reason., Rome (2012) pp. 588–592Google Scholar
  55. 14.55
    P. Balbiani, J.-F. Condotta, L. Farinas Del Cerro: A new tractable subclass of the rectangle algebra, Proc. 16th Int. Jt. Conf. Artif. Intell., Stockholm (1999) pp. 442–447Google Scholar
  56. 14.56
    M. Mansouri, F. Pecora: A representation for spatial reasoning in robotic planning, Proc. IROS Workshop AI-Based Robotics, Tokyo (2013)Google Scholar
  57. 14.57
    M. Mansouri, F. Pecora: More knowledge on the table: Planning with space, time and resources for robots, IEEE Int. Conf. Robotics Autom. (ICRA), Hong Kong (2014) pp. 647–654Google Scholar
  58. 14.58
    M. Skubic, D. Perzanowski, S. Blisard, A. Schultz, W. Adams, M. Bugajska, D. Brock: Spatial language for human-robot dialogs, IEEE Trans. Syst. Man Cybern. C 34(2), 154–167 (2004)CrossRefGoogle Scholar
  59. 14.59
    R. Moratz, T. Tenbrink: Spatial reference in linguistic human-robot interaction: Iterative, empirically supported development of a model of projective relations, Spat. Cogn. Comput. 6(1), 63–107 (2006)Google Scholar
  60. 14.60
    S. Guadarrama, L. Riano, D. Golland, D. Gouhring, Y. Jia, D. Klein, P. Abbeel, T. Darrell: Grounding spatial relations for human-robot interaction, IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Tokyo (2013) pp. 1640–1647Google Scholar
  61. 14.61
    L. Kunze, K.K. Doreswamy, N. Hawes: Using qualitative spatial relations for indirect object search, IEEE Int. Conf. Robotics Autom. (ICRA), Hong Kong (2014) pp. 163–168Google Scholar
  62. 14.62
    L. Mosenlechner, M. Beetz: Parameterizing actions to have the appropriate effects, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., San Francisco (2011) pp. 4141–4147Google Scholar
  63. 14.63
    A. Gaschler, R.P.A. Petrick, M. Giuliani, M. Rickert, A. Knoll: KVP: A knowledge of volumes approach to robot task planning, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Tokyo (2013) pp. 202–208Google Scholar
  64. 14.64
    G. Havur, K. Haspalamutgil, C. Palaz, E. Erdem, V. Patoglu: A case study on the Tower of Hanoi challenge: Representation, reasoning and execution, IEEE Int. Conf. Robotics Autom. (ICRA), Tokyo (2013) pp. 4552–4559Google Scholar
  65. 14.65
    L. de Silva, A.K. Pandey, R. Alami: An interface for interleaved symbolic-geometric planning and backtracking, IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Tokyo (2013) pp. 232–239Google Scholar
  66. 14.66
    L.P. Kaelbling, T. Lozano-Pérez: Hierarchical task and motion planning in the now, IEEE Int. Conf. Robotics Autom. (ICRA) (2011) pp. 1470–1477Google Scholar
  67. 14.67
    F. Lagriffoul, D. Dimitrov, A. Saffiotti, L. Karlsson: Constraint propagation on interval bounds for dealing with geometric backtracking, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Vilamoura (2012) pp. 957–964Google Scholar
  68. 14.68
    G. de Giacomo, L. Iocchi, D. Nardi, R. Rosati: Moving a robot: the KR&R approach at work, Proc. 5th Int. Conf. Princ. Knowl. Represent. Reason. (KR’96), Cambridge (1996) pp. 198–209Google Scholar
  69. 14.69
    R. Hartanto, J. Hertzberg: Fusing DL reasoning with HTN planning, Lect. Notes Comput. Sci. 5243, 62–69 (2008)CrossRefGoogle Scholar
  70. 14.70
    C. Galindo, J.A. Fernandez-Madrigal, J. Gonzalez, A. Saffiotti: Using semantic information for improving efficiency of robot task planning, Proc. ICRA-07 Workshop Semant. Inf. Robotics, Rome (2007) pp. 27–32Google Scholar
  71. 14.71
    W. Cushing, S. Kambhampati, Mausam, D.S. Weld: When is temporal planning really temporal?, Proc. 20th Int. Jt. Conf. Artif. Intell., Hyderabad (2007)Google Scholar
  72. 14.72
    J.L. Bresina, A.K. Jónsson, P.H. Morris, K. Rajan: Activity planning for the Mars exploration rovers, Proc. 15th Int. Conf. Autom. Plan. Sched. (ICAPS), Monterey (2005) pp. 1852–1859Google Scholar
  73. 14.73
    M. Cirillo, F. Pecora, H. Andreasson, T. Uras, S. Koenig: Integrated motion planning and coordination for industrial vehicles, Proc. 24th Int. Conf. Autom. Plan. Sched. (ICAPS), Portsmouth (2014)Google Scholar
  74. 14.74
    S. Fratini, F. Pecora, A. Cesta: Unifying planning and scheduling as timelines in a component-based perspective, Arch. Control Sci. 18(2), 231–271 (2008)MathSciNetzbMATHGoogle Scholar
  75. 14.75
    M. Ghallab, H. Laruelle: Representation and control in IxTeT, a temporal planner, Proc. 2nd Int. Conf. Artif. Intell. Plan. Syst. (AIPS-94), Chicago (1994) pp. 61–67Google Scholar
  76. 14.76
    P. Gregory, D. Long, M. Fox, J.C. Beck: Planning modulo theories: Extending the planning paradigm, Proc. 15th Int. Conf. Autom. Plan. Sched. (ICAPS), São Paulo (2012)Google Scholar
  77. 14.77
    R. Nieuwenhuis, A. Oliveras, C. Tinelli: Solving SAT and SAT modulo theories: From an abstract Davis–Putnam–Logemann–Loveland procedure to DPLL(T), Journal ACM 53, 937–977 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  78. 14.78
    S. Nedunuri, S. Prabhu, M. Moll, S. Chaudhuri, L.E. Kavraki: SMT-based synthesis of integrated task and motion plans for mobile manipulation, IEEE Int. Conf. Robotics Autom. (ICRA), Hong Kong (2014)Google Scholar
  79. 14.79
    U. Köckemann, L. Karlsson, F. Pecora: Grandpa hates robots – Interaction constraints for planning in inhabited environments, Proc. 28th Conf. Artif. Intell., Quebéc City (2014)Google Scholar
  80. 14.80
    M. Di Rocco, F. Pecora, A. Saffiotti: When robots are late: Configuration planning for multiple robots with dynamic goals, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Tokyo (2013)Google Scholar
  81. 14.81
    G.H. Lim, I.H. Suh, H. Suh: Ontology-based unified robot knowledge for service robots in indoor environments, IEEE Trans. Syst. Man Cybern. A 41(3), 492–509 (2011)CrossRefGoogle Scholar
  82. 14.82
    S. Lemaignan, R. Ros, L. Mösenlechner, R. Alami, M. Beetz: ORO, a knowledge management module for cognitive architectures in robotics, Proc. 2010 IEEE/RSJ Int. Conf. Intell. Robots Syst., Taipei (2010) pp. 3548–3553CrossRefGoogle Scholar
  83. 14.83
    M. Tenorth, M. Beetz: KnowRob – A knowledge processing infrastructure for cognition-enabled robots, Int. J. Robotics Res. 32(5), 566–590 (2013)CrossRefGoogle Scholar
  84. 14.84
    M. Daoutis, S. Coradeschi, A. Loutfi: Grounding commonsense knowledge in intelligent systems, J. Ambient Intell. Smart Environ. 1(4), 311–321 (2009)Google Scholar
  85. 14.85
    A. Saffiotti, M. Broxvall, M. Gritti, K. LeBlanc, R. Lundh, J. Rashid, B.S. Seo, Y.J. Cho: The PEIS-Ecology project: Vision and results, IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Nice (2008) pp. 2329–2335Google Scholar
  86. 14.86
    X. Chen, J. Ji, J. Jiang, G. Jin, F. Wang, J. Xie: Developing high-level cognitive functions for service robots, Proc. 9th Int. Conf. Auton. Agents Multiagent Syst., Toronto (2010) pp. 989–996Google Scholar
  87. 14.87
    J.F. Lehman, J.E. Laird, P. Rosenbloom: A gentle introduction to Soar, an architecture for human cognition, Invit. Cogn. Sci. 4, 212–249 (1996)Google Scholar
  88. 14.88
    N. Derbinsky, J.E. Laird: Extending soar with dissociated symbolic memories, Symp. Human Mem. Artif. Agents, AISB (2010) pp. 31–37, Google Scholar
  89. 14.89
    W.G. Kennedy, M. Rouleau, J.K. Bassett: Multiple levels of cognitive modeling within agent-based modeling, Proc. 18th Conf. Behav. Represent. Model. Simul., Sundance (2009) pp. 143–144Google Scholar
  90. 14.90
    R.B. Rusu, Z.C. Marton, N. Blodow, M. Dolha, M. Beetz: Towards 3D point cloud based object maps for household environments, Robotics Auton. Syst. J. Semant. Knowl. Robotics 56(11), 927–941 (2008)CrossRefGoogle Scholar
  91. 14.91
    S. Vasudevan, R. Siegwart: Bayesian space conceptualization and place classification for semantic maps in mobile robotics, Robotics Auton. Syst. 56(6), 522–537 (2008)CrossRefGoogle Scholar
  92. 14.92
    H. Zender, O. Martinez Mozos, P. Jensfelt, G.J.M. Kruijff, W. Burgard: Conceptual spatial representations for indoor mobile robots, Robotics Auton. Syst. 56(6), 493–502 (2008)CrossRefGoogle Scholar
  93. 14.93
    B. Limketkai, L. Liao, D. Fox: Relational object maps for mobile robots, Proc. Int. Jt. Conf. Artif. Intell. (IJCAI) (2005) pp. 1471–1476Google Scholar
  94. 14.94
    M. Tenorth, L. Kunze, D. Jain, M. Beetz: KNOWROB-MAP – Knowledge-linked semantic object maps, 10th IEEE-RAS Int. Conf. Humanoid Robots, Nashville (2010) pp. 430–435Google Scholar
  95. 14.95
    N. Mavridis, D. Roy: Grounded situation models for robots: Where words and percepts meet, Proc. 2006 IEEE/RSJ Int. Conf. Intell. Robots Syst., Beijing (2006) pp. 4690–4697CrossRefGoogle Scholar
  96. 14.96
    D.K. Misra, J. Sung, K. Lee, A. Saxena: Tell me Dave: Context-sensitive grounding of natural language to mobile manipulation instructions, Proc. Robotics Sci. Syst. (RSS) (2014)Google Scholar
  97. 14.97
    T. Kollar, S. Tellex, D. Roy, N. Roy: Toward understanding natural language directions, Proc. 5th AMC/IEEE Int. Conf. Hum.-Robot Interact. (HRI), Osaka (2010) pp. 259–266Google Scholar
  98. 14.98
    C. Matuszek, E. Herbst, L. Zettlemoyer, D. Fox: Learning to parse natural language commands to a robot control system, Proc. 13th Int. Symp. Exp. Robotics (ISER) Québec City (2012) pp. 403–415Google Scholar
  99. 14.99
    F. Duvallet, T. Kollar, A. Stentz: Imitation learning for natural language direction following through unknown environments, 2013 IEEE Int. Conf. Robotics Autom. (ICRA) (2013) pp. 1047–1053CrossRefGoogle Scholar
  100. 14.100
    K. Zhou, M. Zillich, H. Zender, M. Vincze: Web mining driven object locality knowledge acquisition for efficient robot behavior, IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Vilamoura (2012) pp. 3962–3969Google Scholar
  101. 14.101
    M. Tenorth, D. Nyga, M. Beetz: Understanding and executing instructions for everyday manipulation tasks from the World Wide Web, IEEE Int. Conf. Robotics Autom. (ICRA), Anchorage (2010) pp. 1486–1491Google Scholar
  102. 14.102
    M. Tenorth, U. Klank, D. Pangercic, M. Beetz: Web-enabled robots – Robots that use the web as an information resource, Robotics Autom. Mag. 18(2), 58–68 (2011)CrossRefGoogle Scholar
  103. 14.103
    M. Waibel, M. Beetz, R. D’Andrea, R. Janssen, M. Tenorth, J. Civera, J. Elfring, D. Gálvez-López, K. Häussermann, J.M.M. Montiel, A. Perzylo, B. Schießle, O. Zweigle, R. van de Molengraft: RoboEarth – A world wide web for robots, Robotics Autom. Mag. 18(2), 69–82 (2011)CrossRefGoogle Scholar
  104. 14.104
    S. Osentoski, B. Pitzer, C. Crick, G. Jay, S. Dong, D.H. Grollman, H.B. Suay, O.C. Jenkins: Remote robotic laboratories for learning from demonstration – Enabling user interaction and shared experimentation, Int. J. Soc. Robotics 4(4), 449–461 (2012)CrossRefGoogle Scholar
  105. 14.105
    M.B. Blake, S.L. Remy, Y. Wei, A.M. Howard: Robots on the web, IEEE Robotics Autom. Mag. 18, 33–43 (2011)CrossRefGoogle Scholar
  106. 14.106
    D. Hunziker, M. Gajamohan, M. Waibel, R. D’Andrea: Rapyuta: The RoboEarth cloud engine, IEEE Int. Conf. Robotics Autom. (ICRA) (2013) pp. 438–444Google Scholar
  107. 14.107
    D. McDermott: Robot planning, AI Magazine 13(2), 55–79 (1992)Google Scholar
  108. 14.108
    M.E. Pollack, J.F. Horty: There’s more to life than making plans: Plan management in dynamic, multiagent environments, AI Magazine 20(4), 71–83 (1999)Google Scholar
  109. 14.109
    D. McDermott, M. Ghallab, A. Howe, C. Knoblock, A. Ram, M. Veloso, D. Weld, D. Wilkins: PDDL – The Planning Domain Definition Language, Tech. Rep. CVC TR-98-003/DCS TR-1165 (Yale Center for Computational Vision and Control, New Haven 1998)Google Scholar
  110. 14.110
    M. Fox, D. Long: PDDL2.1: An extension of PDDL for expressing temporal planning domains, J. Artif. Intell. Res. 20, 61–124 (2003)zbMATHGoogle Scholar
  111. 14.111
    F. Gravot, S. Cambon, R. Alami: aSyMov: A planner that deals with intricate symbolic and geometric problems, Springer Tracts Adv. Robotics 15, 100–110 (2005)CrossRefGoogle Scholar
  112. 14.112
    R. Alur, T. Henzinger, H. Wong-Toi: Symbolic analysis of hybrid systems, Proc. 37th IEEE Conf. Decis. Control, Tampa (1997) pp. 702–707Google Scholar
  113. 14.113
    R. Alur, T. Henzinger, P. Ho: Automatic symbolic verification of embedded systems, IEEE Trans. Softw. Eng. 22, 181–201 (1996)CrossRefGoogle Scholar
  114. 14.114
    M. Beetz, H. Grosskreutz: Probabilistic hybrid action models for predicting concurrent percept-driven robot behavior, J. Artif. Intell. Res. 24, 799–849 (2005)zbMATHGoogle Scholar
  115. 14.115
    K. Passino, P. Antsaklis: A system and control-theoretic perspective on artificial intelligence planning systems, Appl. Artif. Intell. 3, 1–32 (1989)CrossRefGoogle Scholar
  116. 14.116
    T. Dean, M. Wellmann: Planning and Control (Morgan Kaufmann Publishers, San Mateo 1991)Google Scholar
  117. 14.117
    R. Alami, R. Chatila, S. Fleury, M. Ghallab, F. Ingrand: An architecture for autonomy, Int. J. Robotics Res. 17(4), 315–337 (1998)CrossRefGoogle Scholar
  118. 14.118
    R.P. Bonasso, R.J. Firby, E. Gat, D. Kortenkamp, D.P. Miller, M.G. Slack: Experiences with an architecture for intelligent, reactive agents, J. Exp. Theor. Artif. Intell. 9(2/3), 237–256 (1997)CrossRefGoogle Scholar
  119. 14.119
    D. Andre, S. Russell: Programmable reinforcement learning agents, Proc. 13th Conf. Neural Inf. Process. Syst. (2001) pp. 1019–1025Google Scholar
  120. 14.120
    D. Andre, S.J. Russell: State abstraction for programmable reinforcement learning agents, 18th Natl. Conf. Artif. Intell., Edmonton (2002) pp. 119–125Google Scholar
  121. 14.121
    R.S. Sutton, D. Precup, S.P. Singh: Between MDPs and Semi-MDPs: A framework for temporal abstraction in reinforcement learning, Artif. Intell. 112(1/2), 181–211 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  122. 14.122
    D. Precup: Temporal Abstraction in Reinforcement Learning, Ph.D. Thesis (University of Massachusetts, Amherst 2000)Google Scholar
  123. 14.123
    M. Beetz: Structured reactive controllers, J. Auton. Agents Multi-Agent Syst. 4(1/2), 25–55 (2001)CrossRefGoogle Scholar
  124. 14.124
    D. McDermott: A Reactive Plan Language (Yale University, New Haven 1991)Google Scholar
  125. 14.125
    M. Ingham, R. Ragno, B. Williams: A reactive model-based programming language for robotic space explorers, Proc. 6th Int. Symp. Artif. Intell. Robotics Autom. Space (ISAIRAS) (2001)Google Scholar
  126. 14.126
    M. Bratman: Intention, Plans, and Practical Reason (Harvard Univ. Press, Cambridge 1987)Google Scholar
  127. 14.127
    M. Bratman, D. Israel, M. Pollack: Plan and resource-bounded practical reasoning, Comput. Intell. 4, 349–355 (1988)CrossRefGoogle Scholar
  128. 14.128
    M. Georgeff, F. Ingrand: Decision making in an embedded reasing system, Proc. 11th Int. Jt. Conf. Artif. Intell. (1989) pp. 972–978Google Scholar
  129. 14.129
    M. Beetz, D. Jain, L. Mosenlechner, M. Tenorth, L. Kunze, N. Blodow, D. Pangercic: Cognition-enabled autonomous robot control for the realization of home chore task intelligence, Proceedings IEEE 100(8), 2454–2471 (2012)CrossRefGoogle Scholar
  130. 14.130
    D.S. Weld: An introduction to least commitment planning, AI Magazine 15(4), 27–61 (1994)Google Scholar
  131. 14.131
    D.S. Weld: Recent advances in AI planning, AI Magazine 20(2), 93–123 (1999)Google Scholar
  132. 14.132
    D.V. McDermott: The 1998 AI planning systems competition, AI Magazine 21(2), 35–55 (2000)Google Scholar
  133. 14.133
    J. Hoffmann, B. Nebel: The FF planning system: Fast plan generation through heuristic search, J. Artif. Intell. Res. 14, 253–302 (2001)zbMATHGoogle Scholar
  134. 14.134
    A.L. Blum, M.L. Furst: Fast planning through plan graph analysis, J. Artif. Intell. 90, 281–300 (1997)zbMATHCrossRefGoogle Scholar
  135. 14.135
    F. Bacchus, F. Kabanza: Planning for temporally extended goals, Ann. Math. Artif. Intell. 22(1/2), 5–27 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  136. 14.136
    D. Nau, O. Ilghami, U. Kuter, J.W. Murdock, D. Wu, F. Yaman: SHOP2: An HTN planning system, J. Artif. Intell. Res. 20, 379–404 (2003)zbMATHGoogle Scholar
  137. 14.137
    D. McDermott: Transformational planning of reactive behavior, Tech. Rep. (Yale University, New Haven 1992) Google Scholar
  138. 14.138
    P. H. Winston: Learning Structural Descriptions from Examples, AI Tech. Rep. 231 (MIT, Cambridge 1970) Google Scholar
  139. 14.139
    K.J. Hammond: Case-Based Planning: Viewing Planning as a Memory Task (Academic Press, Waltham 1989)zbMATHCrossRefGoogle Scholar
  140. 14.140
    R.G. Simmons: A theory of debugging plans and interpretations, Proc. 7th Natl. Conf. Artif. Intell. (1988) pp. 94–99Google Scholar
  141. 14.141
    M. Beetz: Concurrent Reactive Plans: Anticipating and Forestalling Execution Failures, Lecture Notes in Artificial Intelligence, Vol. 1772 (Springer, Berlin, Heidelberg, 2000)zbMATHGoogle Scholar
  142. 14.142
    H. Grosskreutz: Probabilistic projection and belief update in the pGOLOG framework. In: Informatik 2000, Informatik Aktuell, ed. by K. Mehlhorn, G. Snelting (Springer, Berlin, Heidelberg 2000) pp. 233–249CrossRefGoogle Scholar
  143. 14.143
    L. Morgenstern: Mid-sized axiomatizations of commonsense problems: A case study in egg cracking, Studia Log. 67(3), 333–384 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  144. 14.144
    N. Blodow, D. Jain, Z.-C. Marton, M. Beetz: Perception and probabilistic anchoring for dynamic world state logging, Proc. 10th IEEE-RAS Int. Conf. Humanoid Robots (Humanoids) (2010) pp. 160–166Google Scholar
  145. 14.145
    N.J. Nilsson: Shakey the Robot, Tech. Note , Vol. TN 323 (SRI International, Stanford 1984)
  146. 14.146
    B. Raphael: The Thinking Computer: Mind Inside Matter (W.H. Freeman, San Francisco 1976)zbMATHGoogle Scholar
  147. 14.147
    Journal of Artificial Intelligence Research:
  148. 14.148
    European Conference on Artificial Intelligence:
  149. 14.149
    AAAI Conference on Artificial Intelligence:
  150. 14.150
    International Conference on Automated Planning and Scheduling:
  151. 14.151
    F. Ingrand, M. Ghallab: Robotics and artificial intelligence: A perspective on deliberation functions, AI Communications 27(1), 63–80 (2014)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Michael Beetz
    • 1
    Email author
  • Raja Chatila
    • 2
  • Joachim Hertzberg
    • 3
  • Federico Pecora
    • 4
  1. 1.Institute for Artificial IntelligenceUniversity BremenBremenGermany
  2. 2.Institute of Intelligent Systems and RoboticsUniversity Pierre et Marie CurieParisFrance
  3. 3.Institute for Computer ScienceOsnabrück UniversityOsnabrückGermany
  4. 4.School of Science and TechnologyUniversity of ÖrebroÖrebroSweden

Personalised recommendations