Richard Dapoigny, Patrick Barlatier, Eric Benoit, Laurent Foulloy


Given a physical system described by a structural decomposition together with additional constraints, a major task in Artificial Intelligence concerns the automatic identification of the system behavior. We will show in the present paper how concepts and techniques from different AI disciplines help solve this task in the case of the intelligent control of engineering systems. Following generative approaches grounded in Qualitative Physics, we derive behavioral specifications from structural and equational information input by the user in the context of the intelligent control of physical systems. The behavioral specifications stem from a teleological representation based on goal structures which are composed of three primitive concepts, i.e. physical entities, physical roles and actions. An ontological representation of goals extracted from user inputs facilitates both local and distributed reasoning. The causal reasoning process generates inferences of possible behaviors from the ontological representation of intended goals. This process relies on an Event Calculus approach. An application example focussing on the control of an irrigation channel illustrates the behavioral identification process.


  1. (1996). Open Distributed Processing - Basic Reference model - Part 2: Foundations. ISO/IEC and ITU-T.
  2. Abadi, M. and Lamport, L. (1993). Composing specifications. ACM Transactions on Programming Languages and Systems, 15(1):73-132.
  3. Barringer, H. (1986). Using temporal logic in the compositional specification of concurrent systems. Technical Report UMCS-86-10-1, Dpt of Computer Science Univ. of Manchester.
  4. Giacomo, G. (1999). Reasoning about nondeterministic and concurrent actions: a process algebra approach. Artificial Intelligence, 107:63-98.
  5. Chittaro, L., Guida, G., Tasso, C., and Toppano, E. (1993). Functional and teleological knowledge in the multimodelling approach for reasoning about physical sytems: a case study in diagnosis. IEEE Transactions on Systems Man and Cybernetics, 23(6):1718-1751.
  6. Cimiano, P., Hotho, A., Stumme, G., and Tane, J. (2004). Conceptual knowledge processing with formal concept analysis and ontologies. In ICFCA, number 2961 in LNAI, pages 189-207. Springer.
  7. Cimiano, P., Staab, S., and Tane, J. (2003). Deriving concept hierarchies from text by smooth formal concept analysis. In Procs. of the GI Workshop LLWA.
  8. Dapoigny, R., Barlatier, P., Benoit, E., and Foulloy, L. (2005). Formal goal generation for intelligent control systems. In 18th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems, number 3533 in LNCS. Springer.
  9. de Coste, D. (1994). Goal-directed qualitative reasoning with partial states. Technical Report 57, Institute for the Learning Sciences, Northwestern University (Evanston).
  10. de Kleer, J. and Brown, J. (1984). A qualitative physics based on confluences. Artificial Intelligence, 24:7-83.
  11. Dooley, K., Skilton, P., and Anderson, J. (1998). Process knowledge bases: Facilitating reasoning through cause and effect thinking. Human Systems Management, 17(4):281-298.
  12. El-Maddah, I. and Maibaum, T. (2003). Goal-oriented requirements analysis for process control systems design. In Procs. of the International Conference on formal methods and models for co-design, pages 45-46. IEEE Comp. Society Press.
  13. et al., Y. U. (1996). Supporting conceptual design based on the function-behavior-state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 10(4):275-288.
  14. Falkenhainer, B. and Forbus, K. (1991). Compositional modeling: finding the right model for the job. Artificial Intelligence, 51:95-143.
  15. Forbus, K. (1984). Qualitative process theory. Artificial Intelligence, 24:85-168.
  16. Freksa, C. (1992). Temporal reasoning based on semiintervals. Artificial Intelligence, 54:199-227.
  17. Galton, A. (1987). Temporal logics and their applications. Academic Press.
  18. Galton, A. and Augusto, J. (2000). Two approaches to event definition. In Springer, number 2453 in LNCS, pages 547-556.
  19. Ganter, B. and Wille, R. (1999). Formal concept analysis - mathematical foundations. Springer.
  20. Giorgini, P., Nicchiarelli, E., Mylopoulos, J., and Sebastiani, R. (2002). Reasoning with goal models. In Procs. of the int. conf. on Conceptual Modeling, number 2503 in LNCS, pages 167-181. Springer.
  21. Gruber, G. and Olsen, G. (1994). An ontology for engineering mathematics. In Doyle, J., Torasso, P., and Sandewall, E., editors, Fourth International Conference on Principles of Knowledge Representation and Reasoning, pages 258-269. Morgan Kaufmann.
  22. Hertzberg, J. and Thiebaux, S. (1994). Turning an action formalism into a planner: a case study. Journal of Logic and Computation, 4:617-654.
  23. Kent, R. (2003). Distributed conceptual structures. In in Procs. of the 6th Int. Workshop on Relational Methods in Computer Science, number 2561 in LNCS. Springer.
  24. Kitamura, Y., Sano, T., Namba, K., and Mizoguchi, R. (2002). A functional concept ontology and its application to automatic identification of functional structures. Artificial Intelligence in Engineering, 16(2):145-163.
  25. Kmenta, S., Fitch, P., and Ishii, K. (1999). Advanced failure modes and effects anlysis of complex processes. In Procs. of the ASME Design Engineering Technical Conferences, Las Vegas (NE).
  26. Larsson, J. (1996). Diagnosis based on explicit means-end models. Artificial Intelligence, 80:29-93.
  27. Lifschitz, V. (1993). A theory of actions. In Procs. of the tenth International Joint Conference on Artificial Intelligence, pages 432-437. Morgan Kaufmann.
  28. Lind, M. (1994). Modeling goals and functions of complex industrial plant. Journal of Applied Artificial Intelligence, 8:259-283.
  29. Ma, J. and Knight, B. (1996). A reified temporal logic. The Computer Journal, 39(9):800-807.
  30. Manna, Z. and Pnueli, A. (1992). The temporal logic of reactive and concurrent systems (Specification). Springer-Verlag.
  31. McDermott, D. (1982). A temporal logic for reasoning about processes and plans. Cognitive Science, 6:101- 155.
  32. Rolland, C., Souveyet, C., and Achour, C. B. (1998). Guiding goal modelling using scenarios. IEEE Transactions on software engineering, pages 1055-1071.
  33. Russo, A., Miller, R., Nuseibeh, B., and Kramer, J. (2001). An abductive approach for analysing event-based requirements specifications. Technical Report DoC2001/7, Dpt of Computing Imperial College, London.
  34. Shanahan, M. (1997). Solving the frame problem: A mathematical investigation of the common sense law of inertia. MIT Press.
  35. Stumme, G. (1999). Hierarchies of conceptual scales. In Procs. of the Workshop on Knowledge Acquisition, Modeling and Managenent (KAW'99), volume 2, pages 78-95.

Paper Citation

in Harvard Style

Dapoigny R., Barlatier P., Benoit E. and Foulloy L. (2005). DERIVING BEHAVIOR FROM GOAL STRUCTURE FOR THE INTELLIGENT CONTROL OF PHYSICAL SYSTEMS . In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 972-8865-29-5, pages 11-18. DOI: 10.5220/0001162300110018

in Bibtex Style

author={Richard Dapoigny and Patrick Barlatier and Eric Benoit and Laurent Foulloy},
booktitle={Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},

in EndNote Style

JO - Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
SN - 972-8865-29-5
AU - Dapoigny R.
AU - Barlatier P.
AU - Benoit E.
AU - Foulloy L.
PY - 2005
SP - 11
EP - 18
DO - 10.5220/0001162300110018