Intelligent Service Robotics

, Volume 10, Issue 3, pp 159–171 | Cite as

A light non-monotonic knowledge-base for service robots

  • Luis A. Pineda
  • Arturo Rodríguez
  • Gibran Fuentes
  • Caleb Rascón
  • Ivan Meza
Original Research Paper


In this paper a Non-Monotonic Knowledge-Base (KB) for practical applications in service robots is presented. The KB is defined as a conceptual hierarchy with inheritance that supports the expression of defaults and exceptions. All classes and individuals, with their properties and relations, can be updated dynamically and the KB-System supports non-monotonic behavior. Non-monotonicity is handled on the basis of a specificity criteria, such that more specific properties and relations have precedence over more general ones. The system supports the expression of conceptual (or terminological) and factual (or assertional) knowledge, which are used in inference in a coherent and consistent way. The KB-System is embedded within the IOCA Architecture, where knowledge about how to communicate and interact with the world, and also knowledge of the particular interpretation situation are represented. The cognitive architecture is structured around a main communication cycle, and queries and conceptual inferences are performed on demand during the interaction of the robot with other agents or the world. The overall structure of the KB with its main interpreter and supporting utilities as well as the embedding of the KB-system in the robot’s architecture are also presented. The KB-System is illustrated with a case study in service robots scenarios, where a practical non-monotonic KB is required. Finally, the implementation of the KB-System in the robot Golem-III is described.


Knowledge representation in service robots Non-monotonic KB-systems The Golem-III robot 



We thank the support of Mauricio Reyes, Hernando Ortega, Noé Hernández, Ricardo Cruz, Varinia Estrada and the members of the Golem Group who participated in the development of the robot Golem-III. We also acknowledge the support of Grants CONACYT’s 178673, ICYTDF-209/12 and PAPIIT-UNAM’s IN-107513 and IN-109816.


  1. 1.
    Ahlrichs U, Fischer J, Denzler J, Drexler C, Niemann H, Noth E, Paulus D (1999) Knowledge based image and speech analysis for service robots. In: Proceedings of the integration of speech and image understanding, pp 21–47Google Scholar
  2. 2.
    Anderson JR, Bower GH (1980) Human associative memory: a brief edition. Lawrence Erlbaum Associates, Publishers, Hillsdale, New JerseyGoogle Scholar
  3. 3.
    Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF (2010) The description logic handbook: theory, implementation, and applications. Cambridge University Press, CambridgezbMATHGoogle Scholar
  4. 4.
    Becker J, Bersch C, Pangercic D, Pitzer B, Rühr T, Sankaran B, Sturm J, Stachniss C, Beetz M, Burgard W (2011) The pr2 workshop-mobile manipulation of kitchen containers. In: IROS workshop on results, challenges and lessons learned in advancing robots with a common platform, vol 120Google Scholar
  5. 5.
    Brachman RJ, Schmolze JG (1985) An overview of the kl-one knowledge representation system. Cognit Sci 9(2):171–216CrossRefGoogle Scholar
  6. 6.
    Brewka G, Eiter T, Truszczyński M (2011) Answer set programming at a glance. Commun ACM 54(12):92–103CrossRefGoogle Scholar
  7. 7.
    Burghart C, Mikut R, Stiefelhagen R, Asfour T, Holzapfel H, Steinhaus P, Dillmann R (2005) A cognitive architecture for a humanoid robot: a first approach. In: Proceedings of the IEEE-RAS international conference on humanoid robots, pp 357–362Google Scholar
  8. 8.
    Chen X, Ji J, Jiang J, Jin G, Wang F, Xie J (2010) Developing high-level cognitive functions for service robots. In: Proceedings of the international conference on autonomous agents and multiagent systems, vol 1, pp 989–996Google Scholar
  9. 9.
    Chen X, Lu D, Chen K, Chen Y, Wang N (2014) KeJia : the intelligent service robot for RoboCup@Home 2014. Tech. Rep., Multi-Agent Systems Lab., Department of Computer Science and Technology, University of Science and Technology of ChinaGoogle Scholar
  10. 10.
    Doyle J (1979) Artificial intelligence. Truth Maint Syst 12(3):251–272Google Scholar
  11. 11.
    Fan Z, Tosello E, Palmia M, Pagello E (2014) In: Proceedings of the international conference intelligent autonomous systemsGoogle Scholar
  12. 12.
    Galindo C, Saffiotti A, Coradeschi S, Buschka P, Fernandez-Madrigal JA, Gonzalez J (2005) Multi-hierarchical semantic maps for mobile robotics. In: Proccedings of the IEEE/RSJ international conference on intelligent robots and systems, pp 2278–2283Google Scholar
  13. 13.
    Galindo C, andez Madrigal JAF, alez JG, Saffiotti A (2008) Robot task planning using semantic maps. Robot Auton Syst 56(11):955–966CrossRefGoogle Scholar
  14. 14.
    Giunchiglia E, Lee J, Lifschitz V, McCain N, Turner H (2004) Nonmonotonic causal theories. Artif Intell 153(1–2):49–104MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Hagras HA (2004) A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans Fuzzy Syst 12(4):524–539CrossRefGoogle Scholar
  16. 16.
    Hawes N, Hanheide M, Hargreaves J, Page B, Zender H, Jensfelt P (2011) Home alone: autonomous extension and correction of spatial representations. In: robotics and automation (ICRA), 2011 IEEE international conference on, pp 3907–3914Google Scholar
  17. 17.
    Hoffmann F, Pfister G (1997) Evolutionary design of a fuzzy knowledge base for a mobile robot. Int J Approx Reason 17(4):447–469CrossRefzbMATHGoogle Scholar
  18. 18.
    Ivaldi S, Nguyen SM, Lyubova N, Droniou A, Padois V, Filliat D, Oudeyer PY, Sigaud O (2014) Object learning through active exploration. IEEE Trans Auton Ment Dev 6(1):56–72CrossRefGoogle Scholar
  19. 19.
    Karg M, Kirsch A (2012) Acquisition and use of transferable, spatio-temporal plan representations for human-robot interaction. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, pp 5220–5226Google Scholar
  20. 20.
    Kollar T, Perera V, Nardi D, Veloso M (2013) Learning environmental knowledge from task-based human-robot dialog. In: Proceedings of the IEEE international conference on robotics and automation, pp 4304–4309Google Scholar
  21. 21.
    Lemaignan S (2012) Grounding the interaction: knowledge management for interactive robots. Ph.D. thesis, Université de ToulouseGoogle Scholar
  22. 22.
    Lemaignan S, Ros R, Msenlechner L, Alami R, Beetz M (2010) Oro, a knowledge management platform for cognitive architectures in robotics. In: Intelligent robots and systems (IROS), 2010 IEEE/RSJ international conference on, pp 3548–3553Google Scholar
  23. 23.
    Lemaignan S, Ros R, Alami R, Beetz M (2011) What are you talking about? grounding dialogue in a perspective-aware robotic architecture. In: Proceedings of the IEEE RO-MAN, pp 107–112Google Scholar
  24. 24.
    Lemaignan S, Ros R, Sisbot EA, Alami R, Beetz M (2012) Grounding the interaction: anchoring situated discourse in everyday human-robot interaction. Int J Soc Robot 4(2):181–199CrossRefGoogle Scholar
  25. 25.
    Lim GH, Suh IH, Suh H (2011) Ontology-based unified robot knowledge for service robots in indoor environments. IEEE Trans Syst Man Cybern Part A Syst Hum 41(3):492–509CrossRefGoogle Scholar
  26. 26.
    MacGregor R, Burstein MH (1991) Using a description classifier to enhance knowledge representation. IEEE Expert 6(3):41–46CrossRefGoogle Scholar
  27. 27.
    Pangercic D, Tenorth M, Jain D, Beetz M (2010) Combining perception and knowledge processing for everyday manipulation. In: IEEE/RSJ international conference on intelligent robots and systems, pp 1065–1071Google Scholar
  28. 28.
    Petković D, Issab M, Pavlović ND, Zentnerb L, Žarko Ćojbašić (2012) Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst Appl 39(18):13,295–13,304CrossRefGoogle Scholar
  29. 29.
    Petković D, Shamshirband S, Anuar NB, Sabri AQM, Rahman ZBA, Pavlović ND (2016) Input displacement neuro-fuzzy control and object recognition by compliant multi-fingered passively adaptive robotic gripper. J Intell Robot Syst 82:177–187CrossRefGoogle Scholar
  30. 30.
    Pineda LA, Meza I, Aviles H, Gershenson C, Rascon C, Alvarado M, Salinas L (2011) IOCA: interaction-oriented cognitive architecture. Res Comput Sci 54:273–284Google Scholar
  31. 31.
    Pineda LA, Rodríguez A, Fuentes G, Rascon C, Meza IV (2015) Concept and functional structure of a service robot. Int J Adv Robot Syst 12:6. doi: 10.5772/60026
  32. 32.
    Pineda LA, Salinas L, Meza IV, Rascon C, Fuentes G (2013) SitLog: a programming language for service robot tasks. Int J Adv Robot Syst 10:358. doi: 10.5772/56906
  33. 33.
    Reiter R (1980) A logic for default reasoning. Artif Intell 13:81–132MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Schiffer S, Ferrein A, Lakemeyer G (2012) Reasoning with qualitative positional information for domestic domains in the situation calculus. J Intell Robot Syst Theory Appl 66(1–2):273–300CrossRefGoogle Scholar
  35. 35.
    Schiffer S, Hoppe N, Lakemeyer G (2013) Natural language interpretation for an interactive service robot in domestic domains. In: Filipe J, Fred A (eds) Agents and artificial intelligence, communications in computer and information science, vol 358. Springer, Berlin Heidelberg, pp 39–53Google Scholar
  36. 36.
    Schmidt-Rohr SR, Knoop S, Lösch M (2008) Bridging the gap of abstraction for probabilistic decision making on a multi-modal service robot. In: robotics: science and systems IV, pp 105–110Google Scholar
  37. 37.
    Schmidt-Rohr SR, Dirschl G, Meissner P, Dillmann R (2011) A knowledge base for learning probabilistic decision making from human demonstrations by a multimodal service robot. In: Proceedings of the international conference on advanced robotics, pp 235–240Google Scholar
  38. 38.
    Seraji H, Howard A (2002) Behavior-based robot navigation on challenging terrain: a fuzzy logic approach. IEEE Trans Robot Autom 18(3):308–321CrossRefGoogle Scholar
  39. 39.
    Sisbot EA, Ros R, Alami R (2011) Situation assessment for human-robot interactive object manipulation. In: RO-MAN, pp 15–20Google Scholar
  40. 40.
    Strasser C, Antonelli GA (2014) Non-monotonic logic. In: Zalta Ward N (ed) The stanford encyclopedia of philosophy (Winter 2014 Edition). Academic Press, New YorkGoogle Scholar
  41. 41.
    Stückler J, Behnke S (2011) Improving people awareness of service robots by semantic scene knowledge. In: Ruiz-del Solar J, Chown E, Plöger PG (eds) RoboCup 2010. Springer, Heidelberg, pp 157–168Google Scholar
  42. 42.
    Tenorth M, Beetz M (2013) Knowrob: a knowledge processing infrastructure for cognition-enabled robots. Int J Robot Res 32(5):566–590Google Scholar
  43. 43.
    Tenorth M, Beetz M (2015) Representations for robot knowledge in the KnowRob framework. Artif Intell. doi: 10.1016/j.artint.2015.05.010
  44. 44.
    Tenorth M, Kunze L, Jain D, Beetz M (2010) Knowrob-map - knowledge-linked semantic object maps. In: IEEE-RAS international conference on humanoid robots, pp 430–435Google Scholar
  45. 45.
    Tulving E (2013) Memory systems: episodic and semantic memory. In: Tulving E, Donaldson W (eds) Organization of memory. Academic Press, New York, pp 381–403Google Scholar
  46. 46.
    Zhang S, Sridharan M, Gelfond M, Wyatt J (2012) KR3: An architecture for knowledge representation and reasoning in robotics. In: Proceedings of the international workshop on non-monotonic reasoningGoogle Scholar
  47. 47.
    Zhang S, Sridharan M, Sheng Bao F (2012) ASP+POMDP: integrating non-monotonic logic programming and probabilistic planning on robots. In: Proceedings of the IEEE international conference on development and learning and epigenetic roboticsGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer Science, Instituto de Investigaciones en Matemáticas Aplicadas y en SistemasUniversidad Nacional Autónoma de MéxicoCoyoacán, MéxicoMexico
  2. 2.Consejo Nacional de Ciencia y Tecnología (CONACyT), Commissioned to: Instituto de Investigaciones en Matemáticas Aplicadas y en SistemasUniversidad Nacional Autónoma de MéxicoCoyoacán, MéxicoMexico

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