Many artificial intelligence systems implicitly use notions of granularity in reasoning, but there is very little research into granularity itself. An exception is the work of Hobbs, who outlines several characteristics of granularity. In this paper we describe an approach to representing granularity which formalizes in computational terms most of Hobbs' notions, often refining and extending them. In particular two types of granularity have been delineated: aggregation and abstraction. Objects can be described at various grain sizes and connected together into a granularity hierarchy which allows focus shifts along either aggregation or abstraction dimensions. Granularity hierarchies can be used in recognition. An especially good domain for granularity-based recognition is educational diagnosis. The ability in an intelligent tutoring system to be able to recognize student behaviour at varying grain sizes is important both for pedagogical reasons (in order to respond to the student at v...

. This paper suggests that a distinction between knowledge acquisition methods should be made. On the one hand there are methods which aim to help the expert and knowledge engineer analyse what knowledge is involved in solving a particular type of problem and how this problem solving is carried out. These methods are concerned with classifying the different types of problem solving and providing tools and methods to help the knowledge engineer identify the appropriate approach and ensure nothing is omitted.. A different approach to knowledge acquisition focuses on ensuring incremental addition of validated knowledge as mistakes are discovered (validated knowledge here means only that the earlier performance of the system is not degraded by the addition of new knowledge). The organisation of this knowledge is managed by the system rather than the expert and knowledge engineer. This would seem to correspond to human incremental development of expertise. From this perspective...

Various proposals for automating the discovery of Web services are available. Most of them regard the terms service and Web service as synonymous. We believe these two terms are not equivalent and it is relevant to distinguish them and to explore their relation. We argue that this fundamental distinction is necessary in order to achieve scalable and realistic Semantic Web service Discovery. Thus, we advocate a proper conceptual model following the distinction of these notions. Our model takes into account Goals, Abstract Goals, Web services and Services, in order to enable efficient and scalable discovery which matches the expectations of service requesters.

Various proposals for automating the discovery of Web services are available. Most of them regard the terms service and Web service as synonymous. We believe these two terms are not equivalent and it is relevant to distinguish them and to explore their relation. We argue that this fundamental distinction is necessary in order to achieve scalable and realistic Semantic Web service Discovery. Thus, we advocate a proper conceptual model following the distinction of these notions. Our model takes into account Goals, Abstract Goals, Web services and Services, in order to enable efficient and scalable discovery which matches the expectations of service requesters.