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

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.KeywordsGeneric ModuleLinguistic TermApproximate ReasoningIdenti CationClassi CationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.