Skip to main content
Log in

The role of expert systems in vegetation science

  • Published:
Vegetatio Aims and scope Submit manuscript

Abstract

An area of artificial intelligence known as experts systems (or knowledge-based systems) is being applied in many areas of science, technology and commerce. It is likely that the techniques will have an impact on vegetation science and ecology in general. This paper discusses some of those impacts and concludes that the main effects will be in areas of applied ecology especially where ecological expertise is needed either quickly (e.g. disaster management) or across a wide range of ecological disciplines (e.g. land management decisions). Expert systems will provide ecologists with valuable tools for managing data and interacting with other fields of expertise. The impact of expert systems on ecological theory will depend on the degree to which ‘deep knowledge’ (i.e. knowledge based on first principles rather than on more empirical rules) is used in formulating knowledge bases.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Attarwala F. T. & Basden A., 1985. A methodology for constructing expert systems. R&D management 15: 141–149.

    Google Scholar 

  • Basden A., 1983. On the application of expert systems. Int. J. Man-Machine Studies 19: 461–477.

    Google Scholar 

  • Chandrasekaran B. & Mittal S., 1983. Deep versus compiled knowledge approaches to diagnostic problem solving. Int. J. Man-Machine Studies 19: 425–436.

    Google Scholar 

  • Davis J. R., Hoare J. R. L. & Nanninga P. M., 1985. The GEO-KAK fire behaviour and fire effects Expert System. In: J.Walker, J. R.Davis & A. M.Gill (eds), Towards an expert system for fire management at Kakadu National Park. CSIRO Div. Water and Land Resources Tech. Mem. 85/2. pp. 153–169. CSIRO, Canberra.

    Google Scholar 

  • Davis, J. R., Hoare, J. R. L. & Nanninga, P. M., in press. Developing a fire management expert system for Kakadu National Park, Australia. J. Envir. Management.

  • Davis R. & Lenat D. B., 1982. Knowledge-based systems in artificial intelligence. McGraw-Hill, New York.

    Google Scholar 

  • Duda R. O. & Shortcliffe E. H., 1983. Expert systems research. Science 220: 261–268.

    Google Scholar 

  • Feigenbaum E. A. & McCorduck P., 1983. The fifth generation: artificial intelligence and Japan's computer challenge to the world. Michael Joseph, London.

    Google Scholar 

  • Forsyth R., 1984. The architecture of expert systems. In: R.Forsyth (ed.), Expert systems: principles and case studies. pp. 9–17. Chapman & Hall, London.

    Google Scholar 

  • Lenat D. B., 1984. Computer software for intelligent systems. Sci. Amer. 251: 152–160.

    Google Scholar 

  • McLaren R., 1985. Knowledge acquisition by computer induction. R&D Management 15: 159–166.

    Google Scholar 

  • Michalski R. S. & Chilausky R. L., 1980. Knowledge acquisition by encoding expert rules versus inductive learning from examples: An experiment utilizing plant pathology. Int. J. Man-Machine Studies 12: 63–87.

    Google Scholar 

  • Noble I. R., 1985. Fire effects, vital attributes and expert systems. In: J.Walker, J. R.Davis & A. M.Gill (eds), Towards an expert system for fire management at Kakadu National Park. CSIRO Div. Water and Land Resources Tech. Mem. 85/2. pp. 96–103. CSIRO, Canberra.

    Google Scholar 

  • Noble I. R. & Slatyer R. O., 1980. The use of vital attributes to predict successional changes in plant communities subject to recurrent disturbances. Vegetatio 43: 5–21.

    Google Scholar 

  • Pereira, L. M., Oliveira, E. & Sabatier, P., 1984. Expert evaluation in logic of environmental resources through natural language. In: A. Elithorn & R. Banjeri (eds), Artificial and human intelligence. pp. 309–311. Elsevier Science Publishers.

  • Quinlan J. R., 1983. Learning efficient classification procedures and their application to chess end games. In: R. S.Michalski, J. G.Carbonelli & T. M.Mitchell (eds), Machine learning. pp. 463–480. Tioga Press, Palo Alto.

    Google Scholar 

  • Sammut C. A., 1985. Concept development for expert system knowledge bases. Aust. Computer J. 17: 49–55.

    Google Scholar 

  • Shannon R. E., Mayer R. & Adelsberger H. H., 1985. Expert systems and simulation. Simulation 44: 275–284.

    Google Scholar 

  • Starfield A. M. & Bleloch A. L., 1983. Expert systems: An approach to problems in ecological management that are difficult to quantify. J. Envir. Management 16: 261–268.

    Google Scholar 

  • Walker J., Davis J. R. & Gill A. M. (eds), 1985. Towards an expert system for fire management at Kakadu National Park. CSIRO Div. Water and Land Resources Tech. Mem. 85/2. CSIRO, Canberra.

    Google Scholar 

  • Waterman D. A., 1986. A guide to expert systems. Addison-Wesley. Reading Massachusetts.

    Google Scholar 

  • Weiss S. M. & Kulikowski C. A. 1984. A practical guide to designing expert systems. Chapman & Hall, London.

    Google Scholar 

  • Weizenbaum J., 1976. Computer power and human reason. W. H. Freeman & Co. New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Noble, I.R. The role of expert systems in vegetation science. Vegetatio 69, 115–121 (1987). https://doi.org/10.1007/BF00038692

Download citation

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00038692

Keywords

Navigation