Logic, Ontology and Planning: the Robot’s Knowledge

 

 

Logic and Computation Courses

Introductory Course

Logic, Ontology and Planning: the Robot’s Knowledge,
Stefano Borgo (Institute of Cognitive Sciences and Technologies (ISTC)) and Oliver Kutz (Free University of Bozen-Bolzano, Italy)

Week 2, 14:00 – 15:30, Room 224, Floor 1

Robotics is a traditional research area which is rapidly expanding due to the ongoing exploitation of intelligent autonomous agents like self-driving cars and drones, industrial robots for production and humanoids for the elderly. The course focuses on the knowledge a robot needs to act in the environment and to understand what it can possibly do. It introduces and discusses the notions and relationships that are needed to “understand” a generic scenario and shows how to structure an ontology to organize such knowledge. In particular, it focuses on how to understand and model capacities, actions, contexts and environments. The flow of information between the knowledge module and the planning and scheduling modules in a generic artificial agent is presented.

Slides for lecture 1

Slides for lecture 2

Slides for lecture 3

Slides for lecture 4

Slides for lecture 5

 

References

LECTURE 1

  • Barandiaran, X. E. et al. (2009). Defining agency: Individuality, normativity, asymmetry, and spatio-temporality in action. Adaptive Behavior, 17(5), 367-386.
  • Ben-Ari, M. and Mondada, F. (2018). Elements of Robotics. Springer International Publishing.
  • Beer, R. D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72, 173– 215.
  • Christensen, W. D. and Hooker, C. (2000). Autonomy and the emergence of intelligence: Organised interactive construction. Communication and Cognition – Artificial Intelligence, 17(3–4), 133–157.
  • Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press.
  • Franklin, S. and Graesser, A. (1996). Is it an agent, or just a program? A taxonomy for autonomous agents. Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages, LNCS Vol. 1193, pp. 21–35. Berlin: Springer.
  • Hazarika, S. M. and Dixit, U. S. (2018). Robotics: History, Trends, and Future Directions. Introduction to Mechanical Engineering, 213-239.
  • IEEE Standard for Ontologies for Robotics and Automation 2015.
  • Maes, P. (1994). Modelling adaptive autonomous systems. Artificial Life, 1, 135–162.
  • Russell, S. J., and Norvig, P. (1995). Artificial intelligence: A modern approach. Englewood Cliffs, NJ: Prentice Hall.
  • Vermaas, P. E., et al. (2013). The design stance and its artefacts. Synthese 190(6), 1131-1152.

LECTURE 2

  • Borgo, S. (2014). An ontological approach for reliable data integration in the industrial domain. Computers in Industry, 65(9), 1242-1252.
  • Borgo, S. and Hitzler, P. (2018). Some Open Issues after Twenty Years of Formal Ontology. In Formal Ontology in Information Science. In Proceedings of FOIS 2018, IOS Press, Cape Town, South Africa.
  • Borgo, S. and Masolo, C. (2009). Foundational choices in DOLCE. In Handbook on Ontologies (pp. 361-381). Springer, Berlin, Heidelberg.
  • Borgo, S. et al. (2014). Logical Operators for Ontological Modeling. In Proceedings of FOIS 2014, IOS Press, pp. 23-36.
  • Borgo, S. and Pozza, G. (2012). Knowledge objects: a formal construct for material, information and role dependences. Knowledge Management Research & Practice, 10(3), 227-236.
  • Guarino N. 1998. “Formal Ontology and Information Systems”. In Proceedings of FOIS 1998, IOS Press, 3–15.
  • Guarino, N. et al. (2009). What is an ontology?. In Handbook on ontologies (pp. 1-17). Springer, Berlin, Heidelberg.
  • Guarino, N., and Welty, C. (2002). Evaluating ontological decisions with OntoClean. Communications of the ACM, 45(2), 61-65.
  • Mizoguchi, R., and Borgo, S. (2017). A Preliminary Study of Functional Parts as Roles. In Proceedings of the 2nd Workshop on Foundational Ontology (FOUST II) at JOWO 2017, Bozen-Bolzano, Italy.
  • Prévot, L. et al. (2005). Interfacing ontologies and lexical resources. Proceedings of OntoLex 2005-Ontologies and Lexical Resources.
  • Vieu, L. et al. (2008). Artefacts and Roles: Modelling Strategies in a Multiplicative Ontology. In Proceedings of FOIS 2008, pp. 121-134. IOS Press.

LECTURE 3

  • Besold, T R  et al. (2016). A narrative in three acts: Using combinations of image schemas to model events. Biologically Inspired Cognitive Architectures, Elsevier, 2016.
  • Codescu, M. (2017). Ontohub: A semantic repository for heterogeneous ontologies. Journal of Applied Ontology, IOS Press, 2017
  • Confalonieri, R. et al. (2016). Upward Refinement Operators for Conceptual Blending in the Description Logic EL++. Annals of Mathematics and Artificial Intelligence (AMAI), Springer.
  • Eppe, M. et al. (2017). A Computational Framework for Conceptual Blending. Artificial Intelligence, 2017.
  • Hedblom, M. M. et al. (2015). Choosing the Right Path: Image Schema Theory as a Foundation for Concept Invention. Journal of Artificial General Intelligence 6 (1): 22-54, De Gruyter, 2015.
  • Hedblom, M. M. et al. (2016). Image schemas in computational conceptual blending. Cognitive Systems Research 39, 42-57, Elsevier, 2016.
  • Hedblom, M. M. et al. (2017). Between Contact and Support: Introducing a logic for image schemas and directed movement. In 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017)}, Bari, Italy, Springer.
  • Kutz, O. (2015). E pluribus unum: Formalisation, Use-Cases, and Computational Support for Conceptual Blending. In Computational Creativity Research: Towards Creative Machines, Atlantis/Springer, Thinking Machines, 2015.
  • Kutz, O. et al. (2018). The Mouse and the Ball: Towards a cognitively-based and ontologically grounded logic of agency. In Proceedings of FOIS 2018, Cape Town, South Africa, IOS Press, 2018.
  • Porello, D. et al. (2017). Repairing Socially Aggregated Ontologies Using Axiom Weakening. In 20th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2017)}, Nice, France, Springer.

LECTURE 4

  • Borgo, S. et al. (2009). A formal ontological perspective on the behaviors and functions of technical artifacts. AI EDAM, 23:3–21.
  • Borgo, S. et a. (2011). A formalization of functions as operations on flows. Journal of Computing and Information Science in Engineering, 11(3):031007 1–14.
  • Borgo, S. et al. (2016). A planning-based architecture for a reconfigurable manufacturing system. In Proceedings of the 26th International Conference on Automated Planning and Scheduling, pp. 358–366. AAAI Press.
  • Borgo, S. and Leitão, P. (2007). Foundations for a core ontology of manufacturing. In Ontologies (pp. 751-775). Springer, Boston, MA.
  • Chandrasekaran, B. (1994). Functional representation and causal processes. In Advances in Computers, pages 73–143. Academic Press.
  • Chandrasekaran, B. and Josephson, J. R. (1997). Representing function as effect. In Proceedings of the Fifth International Workshop on Advances in Functional Modeling of Complex Technical Systems.
  • Chandrasekaran, B. and Josephson, J. R. (2000). Function in device representation. Engineering with Computers, 16(3/4):162–177.
  • Garbacz, P. et al. (2011). Two ontology-driven formalisations of functions and their comparison. Journal of Engineering Design, 22(11-12):733–764.
  • Hirtz, J. et al. (2001). A functional basis for engineering design: Reconciling and evolving previous efforts. Research in Engineering Design, 13:65–82.
  • Kitamura, Y. et al. (2005). An ontological model of device function and its deployment for engineering knowledge sharing. In Proceedings of the First Workshop FOMI 2005, Castelnuovo del Garda, Italy. CD-ROM.
  • Kitamura, Y. and Mizoguchi, R. (2003). Ontology-based description of functional design knowledge and its use in a functional way server. Expert Systems with Applications, 24:153–166.
  • Rector, A. (1998).
    Thesauri and formal classifications: terminologies for people and machines. Methods Inf Med., 37(4-5):501–9.
  • Sanfilippo, E. M., et al. (2016). Features and Components in Product Models. In Proceedings of FOIS 2016, IOS Press.
  • Sanfilippo, E. M., et al. (2018). Modeling Manufacturing Resources: An Ontological Approach. IFIP 15th International Conference on Product Lifecycle Management (PLM), Torino, Italy.
  • Stone, R. B. and Wood, K. (2000). Development of a functional basis for design. Journal of Mechanical Design, 122(4):359–276.

LECTURE 5

  • Borgo, S. et al. (2018). Knowledge-based adaptive agents for manufacturing domains. Engineering with Computers, 1-25.
  • Borgo, S. et al. (2014). Technical artifacts: An integrated perspective. Applied Ontology, 9(3-4), 217-235.
  • Leidner, D. et al. (2015). Classifying compliant manipulation tasks for automated planning in robotics. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on (pp. 1769-1776). IEEE.
  • Umbrico, A. et al. (2017) PLATINUm: A New Framework for Planning and Acting. In Advances in Artificial Intelligence. AI*IA 2017. LNCS 10640 Springer