This paper proposes a new category of functions of artifacts called meta-function. Although conventional research on functional representation defines function as some kinds of abstraction of changes in objects associated with components, a meta-function of a component represents a role played by a function of the component for another function of another component without mention of such changes specific to the target system. The meta-functions explain the types of collaboration and dependency among functions of components as part of the design rationale of the target artifacts. This paper defines nine types of the meta-functions such as ToEnable and ToDrive, which form part of an ontology of functional concepts. The utility of the ontology in explanation and redesign is also discussed.

This research aims at developing a redesign problem solver which proposes suitable modifications of a given artifact for a new requirement or an inconvenience. We concentrate on the two issues, capturing design rationales of the given artifact and organizing general redesign strategies. For the former issue, we propose an ontology of functional concepts, which enables the redesign system to identify functional structures representing a part of design rationales automatically. It particularly includes a new category of functional concepts called meta-functions for representation of dependency among functions of components. For the latter issue, we describe an ontology of the redesign strategies. Some general strategies for proposing redesign solutions in terms of the concepts in the ontology are shown. It reveals conceptualization behind the redesign strategies and then helps us specialize the Soar architecture into a universal redesign engine.

The word "ontology" has gained a good popularity within the AI community. Ontology is usually viewed as a high-level description consisting of concepts that organize the upper parts of the knowledge base.

This paper introduces our novel ontologies for the generation of fault injection test cases and failure detection. Our first ontology is an extension of a standard fault model that is constructed through decomposition to create a framework that facilitates automatic test generation. The second ontology is based on a set of failure modes and again decomposes them and creates a framework to aid in the automatic detection of system failures. We demonstrate our method and tools with the use of a simulated real-world system. 1.

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