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A review and comparison of ontology-based approaches to robot autonomy

Part of: Ontologies and Standards for Intelligent Systems

Published online by Cambridge University Press:  27 December 2019

Alberto Olivares-Alarcos Open the ORCID record for Alberto Olivares-Alarcos [Opens in a new window] ,
Daniel Beßler ,
Alaa Khamis ,
Paulo Goncalves ,
Maki K. Habib ,
Julita Bermejo-Alonso ,
Marcos Barreto ,
Mohammed Diab ,
Jan Rosell  and
João Quintas
...Show all authors
Alberto Olivares-Alarcos
Affiliation:
Institut de Robòtica i Informàtica Industrial, CSIC-UPC Llorens i Artigas 4-6, 08028Barcelona, Spain e-mail: [email protected]
Daniel Beßler
Affiliation:
Institute for Artificial Intelligence, University of Bremen, Germany e-mail: [email protected]
Alaa Khamis
Affiliation:
Centre for Pattern Analysis and Machine Intelligence, University of Waterloo, Canada
Paulo Goncalves
Affiliation:
IDMEC, Instituto Politécnico de Castelo Branco, Portugal
Maki K. Habib
Affiliation:
The American University in Cairo, Egypt
Julita Bermejo-Alonso
Affiliation:
Universidad Isabel I, Burgos, Spain
Marcos Barreto
Affiliation:
Computer Science Dept., Federal University of Bahia, Brazil
Mohammed Diab
Affiliation:
Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
Jan Rosell
Affiliation:
Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain
João Quintas
Affiliation:
Instituto Pedro Nunes, 3030-199Coimbra, Portugal
Joanna Olszewska
Affiliation:
University of the West of Scotland, UK
Hirenkumar Nakawala
Affiliation:
Department of Computer Science, University of Verona, Italy
Edison Pignaton
Affiliation:
Informatics Institute, Federal University of Rio Grande do Sul, Brazil
Amelie Gyrard
Affiliation:
Knoesis, Wright State University, USA
Stefano Borgo
Affiliation:
Laboratory of Applied Ontology ISTC-CNR, Trento, Italy
Guillem Alenyà
Affiliation:
Institut de Robòtica i Informàtica Industrial, CSIC-UPC Llorens i Artigas 4-6, 08028Barcelona, Spain e-mail: [email protected]
Michael Beetz
Affiliation:
Institute for Artificial Intelligence, University of Bremen, Germany e-mail: [email protected]
Howard Li
Affiliation:
University of New Brunswick, Canada
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Abstract

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.

Type
Review
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e29
Copyright
© Cambridge University Press 2019

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Footnotes

*

Both authors contributed equally to this manuscript

References

Arp, R., Smith, B. & Spear, A. D. 2015. Building Ontologies with Basic Formal Ontology. MIT Press.CrossRefGoogle Scholar
Balakirsky, S.et al. 2017. Towards a robot task ontology standard. In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, V003T04A049–V003T04A049.Google Scholar
Bateman, J.et al. 2017. Heterogeneous ontologies and hybrid reasoning for service robotics: the EASE framework. In Third Iberian Robotics Conference, ROBOT ’17. Sevilla, Spain.CrossRefGoogle Scholar
Beer, J. M., Fisk, A. D. & Rogers, W. A. 2014. Toward a framework for levels of robot autonomy in human-robot interaction. Journal of Human-Robot Interaction 3(2), 74–99.CrossRefGoogle Scholar
Beetz, M., Balint-Benczedi, F.et al. 2015a. RoboSherlock: Unstructured information processing for robot perception. In 2015 IEEE International Conference on Robotics and Automation (ICRA), 1549–1556.Google Scholar
Beetz, M., Bartels, G.et al. 2015b. Robotic agents capable of natural and safe physical interaction with human co-workers. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.CrossRefGoogle Scholar
Beetz, M., Tenorth, M. & Winkler, J. 2015c. Open-EASE – A knowledge processing service for robots and robotics/AI researchers. In IEEE International Conference on Robotics and Automation (ICRA). Finalist for the Best Cognitive Robotics Paper Award, Seattle, Washington, USA.CrossRefGoogle Scholar
Beetz, M., Beßler, D., Haidu, A.et al. 2018. KnowRob 2.0–A 2nd Generation knowledge processing framework for cognition-enabled robotic agents. In International Conference on Robotics and Automation (ICRA).CrossRefGoogle Scholar
Beetz, M., Beßler, D., Winkler, J.et al. 2016. Open robotics research using web-based knowledge services. In International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.CrossRefGoogle Scholar
Beetz, M., Jain, D.et al. 2012. Cognition-enabled autonomous robot control for the realization of home chore task intelligence. Proceedings of the IEEE 100(8), 2454–2471.CrossRefGoogle Scholar
Beetz, M., Möosenlechner, L. & Tenorth, M. 2010. CRAM – A cognitive robot abstract machine for everyday manipulation in human environments. In IROS. IEEE, 1012–1017.Google Scholar
Beßler, D., Pomarlan, M. & Beetz, M. 2018. OWL-enabled assembly planning for robotic agents. In Proceedings of the 2018 International Conference on Autonomous Agents, AAMAS 2018. Finalist for the Best Robotics Paper Award, Stockholm, Sweden.Google Scholar
Beßler, D., Porzel, R.et al. 2019. Foundational models for manipulation activity parsing. In Proceedings of AR and VR Conference: Changing Realities in a Dynamic World. Timothy, J., Dieck, T., Rauschnabel, M. C. & Philipp, A. (eds). Springer.Google Scholar
Borgo, S., Cesta, A.et al. 2019. Knowledge-based adaptive agents for manufacturing domains. Engineering with Computers. 35(3), 755–779.CrossRefGoogle Scholar
Borgo, S., Franssen, M.et al. 2014. Technical artifacts: An integrated perspective. Applied Ontology 9(3–4), 217–235.CrossRefGoogle Scholar
Borst, P., Akkermans, H. & Top, J. 1997. Engineering ontologies. International Journal of Human-Computer Studies 46, 365–406.CrossRefGoogle Scholar
Brooks, R. 1991. Intelligence without representation. Artificial Intelligence 47, 139–159.CrossRefGoogle Scholar
Bruno, B., Young Chong, N.et al. 2017a. Paving the way for culturally competent robots: A position paper. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, 553–560.Google Scholar
Bruno, B., Young Chong, N.et al. 2017b. The CARESSES EU-Japan project: Making assistive robots culturally competent. In Italian Forum of Ambient Assisted Living. Springer, 151–169.Google Scholar
Bruno, B., Menicatti, R.et al. 2018. Culturally-competent human-robot verbal interaction. In 2018 15th International Conference on Ubiquitous Robots (UR). IEEE, 388–395.Google Scholar
Bruno, B., Tommaso Recchiuto, C.et al. 2019. Knowledge representation for culturally competent personal robots: Requirements, design principles, implementation, and assessment. International Journal of Social Robotics 11(3), 515–538.CrossRefGoogle Scholar
Buehler, J. E. & Pagnucco, M. 2014. A framework for task planning in heterogeneous multi robot systems based on robot capabilities. In AAAI. AAAI Press, 2527–2533.Google Scholar
Chandrasekaran, B., Josephson, J. R. & Richard Benjamins, V. 1998. Ontology of tasks and methods. In 11th Workshop on Knowledge Acquisition, Modeling and Management (KAW 1998), Banff, Canada.Google Scholar
Chella, A.et al. 2002. Modeling ontologies for robotic environments. In Proceedings of the 14th International Conference on Software Engineering and Knowledge Engineering, SEKE 2002, Ischia, Italy: ACM, 77–80.Google Scholar
Compton, M.et al. 2012. The SSN ontology of the W3C semantic sensor network incubator group. In Web Semantics: Science, Services and Agents on the World Wide Web 17, 25–32.Google Scholar
Davidson, D. 2001. Essays on Actions and Events: Philosophical Essays. Vol. 1. Oxford University Press on Demand.CrossRefGoogle Scholar
Diab, M., Akbari, A., Rosell, J.et al. 2017. An ontology framework for physics-based manipulation planning. In Iberian Robotics Conference. Springer.CrossRefGoogle Scholar
Diab, M., Akbari, A., Ud Din, M.et al. 2019. PMK–A knowledge processing framework for autonomous robotics perception and manipulation. Sensors 19(5).CrossRefGoogle Scholar
Dix, A. 2009. Human-Computer Interaction. Springer.Google Scholar
Dogmus, Z., Erdem, E. & Patoglu, V. 2015. RehabRobo-Onto: Design, development and maintenance of a rehabilitation robotics ontology on the cloud. In: Robotics and Computer-Integrated Manufacturing 33. Special Issue on Knowledge Driven Robotics and Manufacturing, 100–109.Google Scholar
Dogmus, Z., Erdem, E. & Patoglu, V. 2019. RehabRobo-Query: Answering natural language queries about rehabilitation robotics ontology on the cloud. Semantic Web 103, 605–629.CrossRefGoogle Scholar
Fazel-Zarandi, M. & Fox, M. S. 2013. Inferring and validating skills and competencies over time. Applied Ontology 8(3), 131–177.CrossRefGoogle Scholar
Gangemi, A., Borgo, S.et al. 2004. Task Taxonomies for Knowledge Content D07. Technical report Metokis Project.Google Scholar
Gangemi, A. & Mika, P. 2003. Understanding the semantic web through descriptions and situations. In OTM Confederated International Conferences ‘On the Move to Meaningful Internet Systems’. Springer, 689–706.Google Scholar
Gibson, J. J. 1979. The Ecological Approach to Visual Perception. Houghton Miffin.Google Scholar
Gil, Y. 2005. Description logics and planning. AI Magazine 26(2), 73–84.Google Scholar
Góomez-Pérez, A., Fernández-López, M. & Corcho, O. 2004. Ontological Engineering with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. In 1st. Advanced Information and Knowledge Processing. Springer.Google Scholar
Gonçalves, P. J. S. & Torres, P. M. B. 2015. Knowledge representation applied to robotic orthopedic surgery. Robotics and Computer-Integrated Manufacturing 33, 90–99.CrossRefGoogle Scholar
Gruber, T. 1993. A translation approach to portable ontologies. Knowledge Acquisition 5(2), 199–220.CrossRefGoogle Scholar
Grüninger, M. 2004. Ontology of the process specification language. In Handbook on Ontologies. Springer, 575–592.Google Scholar
Guarino, N. 1998. Formal ontology in information systems. In Proceedings of FOIS 1998, Trento, Italy: IOS Press, Amsterdam, 3–15.Google Scholar
Guarino, N. & Giaretta, P. 1995. Ontologies and knowledge bases: Towards a terminological clarification. In Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing (KBKS 1995) Mars, N. (ed). University of Twente, Enschede, The Netherlands and IOS Press, Amsterdam, The Netherlands, 25–32.Google Scholar
Guarino, N., Oberle, D. & Staab, S. 2009. What is an ontology? In Handbook on Ontologies. Springer, 1–17.Google Scholar
Haage, M.et al. 2011. Declarative-knowledge-based reconfiguration of automation systems using a blackboard architecture. In Eleventh Scandinavian Conference on Artificial Intelligence, Vol. 227. IOS Press, 163–172.Google Scholar
Haidu, A.et al. 2018. KNOWROB-SIM – Game engine-enabled knowledge processing for cognition-enabled robot control. In International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain: IEEE.CrossRefGoogle Scholar
Hesslow, G. 2012. The current status of the simulation theory of cognition. Brain Research 1428, 71–79.CrossRefGoogle ScholarPubMed
Jacobsson, L., Malec, J. & Nilsson, K. 2016. Modularization of skill ontologies for industrial robots. In Proceedings of ISR 2016: 47st International Symposium on Robotics. VDE, 1–6.Google Scholar
Jorge, V. A. M.et al. 2015. Exploring the IEEE ontology for robotics and automation for heterogeneous agent interaction. Robotics and Computer-Integrated Manufacturing 33, 12–20.CrossRefGoogle Scholar
Khaliq, A. A.et al. 2018. Culturally aware planning and execution of robot actions. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 326–332.Google Scholar
Krüger, V.et al. 2007. The meaning of action: A review on action recognition and mapping [English]. Advanced Robotics 21(13), 1473–1501.Google Scholar
Kunze, L., Roehm, T. & Beetz, M. 2011. Towards semantic robot description languages. In IEEE International Conference on Robotics and Automation (ICRA). Shanghai, China, 5589–5595.Google Scholar
Langley, P., Laird, J. E. & Rogers, S. 2009. Cognitive architectures: Research issues and challenges. Cognitive Systems Research 10(2), 141–160.CrossRefGoogle Scholar
Lemaignan, S., Ros, R., Alami, R.et al. 2011. What are you talking about? Grounding dialogue in a perspective-aware robotic architecture. In 2011 RO-MAN. IEEE, 107–112.Google Scholar
Lemaignan, S., Ros, R., Mösenlechner, L.et al. 2010. ORO, a knowledge management platform for cognitive architectures in robotics. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 3548–3553.Google Scholar
Lenat, D. B. & Guha, R. V. 1990. Building Large Knowledge-Based System: Representation and Inference in the Cyc Project. New York: Addison-Wesley.Google Scholar
Lim, G. H. 2019. Shared representations of actions for alternative suggestion with incomplete information. Robotics and Autonomous Systems. 116, 38–50CrossRefGoogle Scholar
Lim, G. H., Suh, I. H. & Suh, H. 2010. Ontology-based unified robot knowledge for service robots in indoor environments. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 41(3), 492–509.CrossRefGoogle Scholar
Lim, G. H., Suh, I. H. & Suh, H. (2011). Ontology-based unified robot knowledge for service robots in indoor environments. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 41(3), 492–509.CrossRefGoogle Scholar
Marconi, L.et al. 2012. The SHERPA project: Smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments. In IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), College Station, Texas, USA.CrossRefGoogle Scholar
Masolo, C. & Borgo, S. 2005. Qualities in formal ontology. In: Foundational Aspects of Ontologies (FOnt 2005) Workshop at KI 2005, 2–16.Google Scholar
Masolo, C.et al. 2003. WonderWeb Deliverable D18: Ontology Library. Technical Report. Laboratory for Applied Ontology-ISTC-CNR.Google Scholar
McDermott, D.et al. 1998. PDDL–The Planning Domain Definition Language. Technical Report CVC TR98003/DCS TR1165. New Haven, CT: Yale Center for Computational Vision and Control.Google Scholar
Menicatti, R., Bruno, B. & Sgorbissa, A. 2017. Modelling the influence of cultural information on vision-based human home activity recognition. In 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 32–38.Google Scholar
Mizoguchi, R., Kitamura, Y. & Borgo, S. 2016. A unifying definition for artifact and biological functions. Applied Ontology 11(2), 129–154.CrossRefGoogle Scholar
Moralez, L. A. 2016. Affordance ontology: Towards a unified description of affordances as events. Res Cogitans 07, 35–45.Google Scholar
Neuhaus, F., Grenon, P. & Smith, B. 2004. A formal theory of substances, qualities, and universals. In: Formal Ontology in Information Systems: Proceedings of the Third International Conference (FOIS-2004). IOS Press.Google Scholar
Niles, I. & Pease, A. 2001a. Towards a standard upper ontology. In Proceedings of the International Conference on Formal Ontology in Information Systems-Volume. ACM.CrossRefGoogle Scholar
Norman, D. A. 2002. The Design of Everyday Things. New York, NY, USA: Basic Books, Inc.Google Scholar
Ortmann, J. & Kuhn, W. 2010. Affordances as qualities. In Proceedings of the 2010 Conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010). Amsterdam, The Netherlands, The Netherlands: IOS Press, 117–130.Google Scholar
Oxford-University Press 2019. Compact Oxford English Dictionary of Current English. http://www.askoxford.com.Google Scholar
Papadimitriou, C. H. 2003. Computational Complexity. John Wiley and Sons Ltd.Google Scholar
Paulius, D. & Sun, Y. 2018. A survey of knowledge representation and retrieval for learning in service robotics. CoRR abs/1807.02192.Google Scholar
Persson, J.et al. 2010. A knowledge integration framework for robotics. In ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics). VDE, 1–8.Google Scholar
Perzylo, A., Grothoff, J., et al. 2019a. Capability-based semantic interoperability of manufacturing resources: A BaSys 4.0 perspective. In Proceedings of the IFAC Conference on Manufacturing Modeling, Management, and Control (MIM), Berlin, Germany.CrossRefGoogle Scholar
Perzylo, A., Rickert, M.et al. 2019b. SMErobotics: Smart robots for flexible manufacturing. IEEE Robotics and Automation Magazine. 26(1), 78–90.Google Scholar
Riva, G. & Riva, E. 2019. SARAFun: Interactive robots meet manufacturing industry. Cyberpsychology, Behavior, and Social Networking 22(4), 295–296.CrossRefGoogle ScholarPubMed
Robot and Robotics Devices – Vocabulary (2012). Standard. International Organization for Standardization.Google Scholar
Ros, R.et al. 2010. Which one? Grounding the referent based on effcient human-robot interaction. In 19th International Symposium in Robot and Human Interactive Communication. IEEE, 570–575.Google Scholar
Rusu, R. B.et al. 2009. Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, USA.CrossRefGoogle Scholar
Salustri, F. A. 2000. Ontological commitments in knowledge-based design software: A progress report. In Knowledge Intensive Computer Aided Design: IFIP TC5 WG5.2 Third Workshop on Knowledge Intensive CAD December 1–4, 1998, Tokyo, Japan . Finger, S., Tomiyama, T. & Mäntylä, M. (eds). Boston, MA: Springer US, 41–72.CrossRefGoogle Scholar
Saxena, A.et al. 2014. Robobrain: Large-scale knowledge engine for robots. arXiv preprint arXiv:1412.0691.Google Scholar
Schlenoff, C.et al. 2012. An IEEE standard ontology for robotics and automation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE.CrossRefGoogle Scholar
Sgorbissa, A.et al. 2018. Encoding guidelines for a culturally competent robot for elderly care. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 1988–1995.Google Scholar
Sirin, E.et al. 2007. Pellet: A practical OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World Wide Web 5(2), 51–53.CrossRefGoogle Scholar
Sisbot, E. A., Ros, R. & Alami, R. 2011. Situation assessment for human-robot interactive object manipulation. In 2011 RO-MAN. IEEE, 15–20.Google Scholar
Stenmark, M., Haage, M.et al. 2018. Supporting semantic capture during kinesthetic teaching of collaborative industrial robots. International Journal of Semantic Computing 12(01), 167–186.CrossRefGoogle Scholar
Stenmark, M. & Malec, J. 2013. Knowledge-based industrial robotics. In SCAI, 265–274.Google Scholar
Stenmark, M,.Malec, J. & Stolt, A. 2015. From high-level task descriptions to executable robot code. In Intelligent Systems’ 2014. Springer, 189–202.Google Scholar
Studer, R., Benjamins, V. R. & Fensel, D. 1998. Knowledge engineering: Principles and methods. IEEE Transactions on Data and Knowledge Engineering 25(1–2), 161–197.CrossRefGoogle Scholar
Suh, I. H.et al. 2007. Ontology-based multi-layered robot knowledge framework (OMRKF) for robot intelligence. In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 429–436.Google Scholar
Tenorth, M., Bartels, G. & Beetz, M. 2014. Knowledge-based specification of robot motions. In Proceedings of the European Conference on Artificial Intelligence (ECAI).Google Scholar
Tenorth, M. & Beetz, M. 2009. KnowRob – Knowledge processing for autonomous personal robots. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 4261–4266.Google Scholar
Tenorth, M. & Beetz, M. 2012. A unified representation for reasoning about robot actions, processes, and their effects on objects. In IROS. IEEE, 1351–1358.Google Scholar
Tenorth, M. & Beetz, M. 2013. KnowRob: A knowledge processing infrastructure for cognition-enabled robots. The International Journal of Robotics Research. 32(5), 566–590.Google Scholar
Tenorth, M. & Beetz, M. 2017. Representations for robot knowledge in the KnowRob framework. Artificial Intelligence. 247, 151–169.Google Scholar
Tenorth, M., Kunze, L.et al. 2010a. KNOWROB-MAP – Knowledge-linked semantic object maps. In 10th IEEE-RAS International Conference on Humanoid Robots, Nashville, TN, USA, 430–435.Google Scholar
Tenorth, M., Nyga, D. & Beetz, M. 2010b. Understanding and executing instructions for everyday manipulation tasks from the World Wide Web. In IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA, 1486–1491.Google Scholar
Thosar, M.et al. 2018. A review of knowledge bases for service robots in household environments. In 6th International Workshop on Artificial Intelligence and Cognition.Google Scholar
Tiddi, I.et al. 2017. An ontology-based approach to improve the accessibility of ROS-based robotic systems. In Proceedings of the Knowledge Capture Conference,. K-CAP 2017, Austin, TX, USA: ACM, 13:1–13:8.Google Scholar
Topp, E. A. & Malec, J. 2018. A knowledge based approach to user support for robot programming. In AI for Multimodal Human Robot Interaction Workshop within the Federated AI Meeting 2018 in Stockholm, 31–34.Google Scholar
Topp, E. A.et al. 2018. Ontology-based knowledge representation for increased skill reusability in industrial robots. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 5672–5678.Google Scholar
Torres, P., Gonçalves, P. J. S. & Martins, J. 2015. Robotic motion compensation for bone movement, using ultrasound images. Industrial Robot: An International Journal 42(5), 466–474.CrossRefGoogle Scholar
Turvey, M. T. 1992. Affordances and prospective control: An outline of the ontology. Ecological Psychology 4(3), 173–187.CrossRefGoogle Scholar
Uschold, M. & Gruninger, M. 1996. Ontologies: Principles, methods and applications. Knowledge Engineering Review 11(2), 93–155.CrossRefGoogle Scholar
Uschold, M.et al. 1998. The enterprise ontology. The Knowledge Engineering Review 13(1), 31–89.CrossRefGoogle Scholar
Vernon, D. 2014. Artificial Cognitive Systems: A Primer. MIT Press.Google Scholar
Vernon, D., Metta, G. & Sandini, G. 2007. A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents. IEEE Transactions on Evolutionary Computation 11(2), 151–180.CrossRefGoogle Scholar
Waibel, M.et al. 2011. Roboearth-a world wide web for robots. IEEE Robotics and Automation Magazine (RAM), Special Issue Towards a WWW for Robots.Google Scholar
Wang, A. Y., Sable, J. H. & Spackman, K. A. 2002. The SNOMED clinical terms development process: refinement and analysis of content. In Proceedings of the AMIA Symposium. American Medical Informatics Association, 845.Google Scholar
Warnier, M.et al. 2012. When the robot puts itself in your shoes. managing and exploiting human and robot beliefs. In 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 948–954.Google Scholar
Yanco, H. A. & Drury, J. L. 2002. A taxonomy for human-robot interaction. In Proceedings of the AAAI Fall Symposium on Human-Robot Interaction, 111–119.Google Scholar
Yanco, H. A. & Drury, J. 2004. Classifying human-robot interaction: An updated taxonomy. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583) vol. 3, 2841–2846.CrossRefGoogle Scholar
Yazdani, F.et al. 2018. Cognition-enabled framework for mixed human-robot rescue team. In International Conference on Intelligent Robots and Systems (IROS). IEEE. Madrid, Spain.CrossRefGoogle Scholar