Artificial Intelligence
A calculus for the construction of modular Prolog programs
Journal of Logic Programming
A specialisation calculus to improve expert systems communications
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
A KADS/(ML)2 model of a scheduling task
Formal specification of complex reasoning systems
Multi-content systems as a specification framework for complex reasoning systems
Formal specification of complex reasoning systems
Advances in Computer Methods for Systematic Biology: Artificial Intelligence, Databases, Computer Vision
Approximate Reasoning Models
Meta-Level Architectures and Reflection
Meta-Level Architectures and Reflection
Reflective reasoning with and between a declarative metatheory and the implementation code
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Fundamenta Informaticae
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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.