Skip to main content
SpringerLink
Log in

Handling imperfect knowledge in Milord II for the identification of marine sponges

  • Chapter
  • First Online:
Applications of Uncertainty Formalisms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1455))

Abstract

In this chapter we present SPONGIA, a knowledge based system implemented using the Milord II programming environment. SPONGIA deals with the identification of sponges from the Atlanto-Mediterranean biogeographical province. It covers the identification of more than 100 taxa of the phylum Porifera from class to species. The effective handling of uncertainty has been critical to display an efficient performance in SPONGIA. This problem has been managed taking advantage of the multiple techniques provided by Milord II. The use of fuzzy logic makes it possible to accurately represent the imprecise knowledge which constitutes the classificatory theory of Porifera to a large extent. It also provides the user with some means of expressing his state of knowledge with accuracy. Easy design and incremental development of the knowledge base are possible thanks to modularity. Taxonomic knowledge is represented by means of plain modules hierarchically interconnected via submodule declarations and refinement operations. To emulate the reasoning strategies we use generic modules, which can take other modules as parameters. Thanks to the uncertainty handling and reflective deduction mechanisms it has been possible to emulate complex reasoning strategies displayed by experts in sponge systematics. Finally, the strict compartmentation of domain knowledge and knowledge concerning reasoning strategies into modules allows the reusability of pieces of knowledge.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J. Agustí, F. Esteva, P. Garcia, L. Godo, R. Lopez de Mantaras, and C. Sierra. Local multi-valued logics in modular expert systems. Journal of Experimental and Theoretical Artificial Intelligence, 6:303–321, 1994.

    Article  Google Scholar 

  2. G. Attardi and M. Simi. A formalisation of viewpoints. Fundamenta Informaticae, 23(2,3,4):149–174, 1995.

    Article  MathSciNet  Google Scholar 

  3. J. Balder, F. Van Harmelen, and M. Aben. A KADS=(ML) 2 model of a scheduling task. In Jan Treur and Thomas Wetter, editors, Formal Specification of Complex Resoning Systems. Ellis Horwood, 1993.

    Google Scholar 

  4. S. J. Barlett and P. Suber, editors. Self-reference: Reflections on reflexivity. Martinus Nijhoff, Dordrecht, 1987.

    Google Scholar 

  5. P.R. Bergquist. Poriferan relationships. In S. Conway-Morris, J.D. George, R. Gibson, and H.M. Platt, editors, The origins and relationships of lower invertebrates, volume 28 of Systematics Association, pages 15–27. 1985. Special Volume.

    Google Scholar 

  6. P.R. Bergquist and P.J. Fromont. The marine Fauna of New Zealand: Porifera demospongiae, part 4 (poecilosclerida). pages 1–21, 1988.

    Google Scholar 

  7. P. Bonissone. Summarizing and propagating uncertain information with triangular norms. International Journal of Approximate Reasoning, 1(1):71–101, 1987.

    Article  MathSciNet  Google Scholar 

  8. N. Boury-Esnault, M.T. Lopes, and M.J. Uriz. Spongiaires bathyaux de la mer d’alboran et du golfe ibero-marocain. Memoires du Museum National d’Histoire Naturelle, 160:174, 1994.

    Google Scholar 

  9. N. Boury-Esnault and K. Rützler, editors. Thesaurus of Terms for Sponges. Smithsonian Institution Press, Washington D.C., USA. In press.

    Google Scholar 

  10. W. J. Clancey. Heuristic classification. Artificial Intelligence, 27(3):289–350, 1985.

    Article  Google Scholar 

  11. R. López de Mántaras. Approximate Reasoning Models. Ellis Horwood series on Artificial Intelligence, UK, 1990.

    Google Scholar 

  12. R. López de Mántaras (ed.). Special issue on Reflection and Meta-level AI Architectures. Future Generation Computer Systems Journal, 12, 1996.

    Google Scholar 

  13. M. Domingo and C. Sierra. A knowledge level analysis of taxonomic domains. International Journal of Intelligent Systems, 12(2):105–135, 1997.

    Article  Google Scholar 

  14. Marta Domingo. An Expert System Architecture for Identification in Biology, volume 4 of Monografies de l’IIIA. IIIA — CSIC, Bellaterra (Barcelona), Spain, 1995.

    Google Scholar 

  15. Marta Domingo. Models of practical taxonomic reasoning in knowledge-based systems: an application to Porifera. Bulletin de l’Institut Royal des Sciences Naturelles de Belgique — Biologie, 66suppl.:27–35, 1996.

    Google Scholar 

  16. M. Edwards and D.R. Morse. The potential for computer-aided identification in biodiversity research. TREE, 10(4):153–158, 1995.

    Google Scholar 

  17. R. Fortuner, editor. Advances in Computer Methods for Systematic Biology: Artificial Intelligence, Databases, Computer Vision. The Johns Hopkins University Press, Baltimore. London, 1993.

    MATH  Google Scholar 

  18. P.J. Fromont and P.R. Bergquist. Structural characters and their use in sponge taxonomy: When is a sigma not a sigma. In K. Rützler, editor, New perspectives in sponge biology, pages 273–278. Smithsonian Institution Press, Washington D.C., USA, 1990.

    Google Scholar 

  19. F. Giunchiglia and P. Traverso. Reflective reasoning with and between a declarative metatheory. In IJCAI-91, pages 111–117, 1991.

    Google Scholar 

  20. F. Giunchiglia, P. Traverso, and E. Giunchiglia. Multi-context systems as a specification framework for complex reasoning systems. In Jan Treur and Thomas Wetter, editors, Formal Specification of Complex Resoning Systems. Ellis Horwood, 1993.

    Google Scholar 

  21. L. Godo and C. Sierra. Knowledge base refinement in Milord. In Proceedings of 14th IMACS World Congress, Atlanta, USA, 1994.

    Google Scholar 

  22. P. Hajek, L. Godo, and F. Esteva. Fuzzy logic and probability. In P. Besnard and S. Hanks, editors, Proceedings of the Uncertainty in Artificial Intelligence Conference, UAI-95, pages 237–244, San Francisco, USA, 1995. Morgan Kaufmann.

    Google Scholar 

  23. R. Harper, D. MacQueen, and R. Millner. The Definition of Standard ML. Technical Report ECS-LFCS-86-2, Dept. Computer Science, Univ. of Edinburgh, 1986.

    Google Scholar 

  24. A. Hunter and S. Parsons, editors. Uncertainty in Information Systems, volume This volume. Springer.

    Google Scholar 

  25. J. Puyol, L. Godo, and C. Sierra. Specialisation calculus and communication. International Journal of Approximate Reasoning, 1998.

    Google Scholar 

  26. N. Knowtoln, E. Weil, L.A. Weigt, and H.M. Guzman. Sibling species in montastraea annularis, coral bleaching and the coral climate record. Science, 255:330–333, 1992.

    Article  Google Scholar 

  27. C. Lévi. Nouveau spongiaires lithistides bathyaux affinites cretacees de la nouvellecaledonie. Bull. Mus. nat. Hist. nat. Paris, 10(2):241–263, 1988.

    Google Scholar 

  28. P. Maes and N. Nardi, editors. Meta-level Architectures and Reflection. Academic Press, Amsterdam, 1988.

    MATH  Google Scholar 

  29. R. Martin-Clouaire and H. Prade. SPII-1, a simple inference engine capable of accommodating both imprecision and uncertainty in expert systems. In G. Mitra, editor, Computer-Assisted Decision Making, LNCS, pages 117–131. North Holland, 1986.

    Google Scholar 

  30. R. J. Pankhurst. Practical Taxonomic Computing. Cambridge University Press, Cambridge, 1991.

    Google Scholar 

  31. S. Parsons. Qualitative approaches to reasoning under uncertainty. MIT Press, Cambridge, USA, (in press), 1997.

    Google Scholar 

  32. J. Pavelka. On fuzzy logic I, II, III. Zeitschr. f. Math. Logik und Grundl. der Math., 25:45–52, 119–134, 447–464, 1979.

    Article  MathSciNet  Google Scholar 

  33. J. Puyol and C. Sierra. Milord ii: Language description. Mathware & Soft Computing, 4:299–338, 1997.

    Google Scholar 

  34. Josep Puyol. Modularization, Uncertainty, Reflective Control and Deduction by Specialization in Milord II, a Language for Knowledge-Based Systems. PhD thesis, Universitat Autònoma de Barcelona, Barcelona, 1994.

    Google Scholar 

  35. Josep Puyol, Lluís Godo, and Carles Sierra. A specialization calculus to improve expert system communication. In ECAI’92, pages 144–148, Viena, 1992.

    Google Scholar 

  36. D. Sannella and A. Tarlecki. Foundations of Algebraic Specification and Formal Program Development. Cambridge University Press, Cambridge, (in press) edition, 1997.

    MATH  Google Scholar 

  37. D. T. Sannella and L. A. Wallen. A Calculus for the Construction of Modular Prolog Programs. The Journal of Logic Programming, pages 147–177, 1992.

    Google Scholar 

  38. A.M. Sole-Cava and J.P. Thorpe. High levels of genetic variation in marine sponges. In K. Rutzler, editor, New perspectives in sponge biology, pages 322–337. Smithsonian Institution Press, Washington D.C., USA, 1990.

    Google Scholar 

  39. Y. H. Tan and J. Treur. A bi-modular approach to non-monotonic reasoning. In Proc. First World Congress on the Fundamentals of AI, WOCFAI-91, pages 461–475, Paris, 1991.

    Google Scholar 

  40. J. Treur. On the use of reflection principles in modelling complex reasoning. International Journal of Intelligent Systems, 6:277–294, 1992.

    Article  Google Scholar 

  41. M.J. Uriz, D. Martin, and D. Rosell. Relationships between taxonomical and biological characteristics and chemically mediated bioactivity in mediterranean sponges. Marine Biology, 113:287–297, 1992.

    Google Scholar 

  42. L. A. Zadeh. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1:3–28, 1978.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence Research Institute, IIIA, Spanish Scientific Research Council, CSIC, Campus UAB, 08193, Bellaterra, Barcelona, Spain

    Marta Domingo, Lluís Godo & Carles Sierra

Authors
  1. Marta Domingo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lluís Godo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carles Sierra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Anthony Hunter

  2. Queen Mary and Westfield College, Department of Electronic Engineering, University of London, London, E1 4NS, UK

    Simon Parsons

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Domingo, M., Godo, L., Sierra, C. (1998). Handling imperfect knowledge in Milord II for the identification of marine sponges. In: Hunter, A., Parsons, S. (eds) Applications of Uncertainty Formalisms. Lecture Notes in Computer Science(), vol 1455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49426-X_6

Download citation

  • DOI: https://doi.org/10.1007/3-540-49426-X_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65312-7

  • Online ISBN: 978-3-540-49426-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation