摘要:

The paper describes the modeling of a user's conceptual knowledge in the general user modeling shell system BGP‐MS. On the one hand, BGP‐MS is a workbench for the develment of a user model in a particular application domain. It supports the definition of the architecture of the individual user model, and of the architecture and the contents of user stereotypes. A rich representation language for conceptual knowledge, a partition mechanism, and flexible graphics‐based interfaces are at the disposal of the user model developer. On the other hand, BGP‐MS is a runtime user modeling tool aimed at fulfilling central tasks of a user modeling component in an application system. In this mode, the system offers a functional interface for accessing and updating the model of the current user, as well as basic domain‐independent inference mechanisms, support for inferences defined by the developer, and a customizable stereotype management utility. The usefulness of BGP‐MS will be demonstrated by illustrating how it can support the user modeling of various recently developed applicat

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1990.tb00295.x

出版商:Blackwell Publishing Ltd    

年代:1990

数据来源: WILEY

摘要:

This paper presents a logical formalism for representing and reasoning with statistical knowledge. One of the key features of the formalism is its ability to deal with qualitative statistical information. It is argued that statistical knowledge, especially that of a qualitative nature, is an important component of our world knowledge and that such knowledge is used in many different reasoning tasks. The work is further motivated by the observation that previous formalisms for representing probabilistic information are inadequate for representing statistical knowledge. The representation mechanism takes the form of a logic that is capable of representing a wide variety of statistical knowledge, and that possesses an intuitive formal semantics based on the simple notions of sets of objects and probabilities defined over those sets. Furthermore, a proof theory is developed and is shown to be sound and complete. The formalism offers a perspicuous and powerful representational tool for statistical knowledge, and a proof theory which provides a formal specification for a wide class of deductive inferences. The specification provided by the proof theory subsumes most probabilistic inference procedures previously developed in AI. The formalism also subsumes ordinary first‐order logic, offering a smooth integration of logical and statistical knowledg

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1990.tb00296.x

出版商:Blackwell Publishing Ltd    

年代:1990

数据来源: WILEY

摘要:

While there has been recent interest in research on planning and reasoning about actions, nearly all research results have been theoretical. We know of no previous examples of a planning system that has made a significant impact on a problem of practical importance. One of the primary goals during the development of the SIPE‐2 planning system has been the balancing of efficiency with expressiveness and flexibility. With a major new extension, SIPE‐2 has begun to address practical problems. This paper describes this new extension and the new applications of the planner. One of these applications is the problem of producing products from raw materials on process lines under production and resource constraints. This is a problem of commercial importance and SiPE‐2's application to it is described in some d

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1990.tb00297.x

出版商:Blackwell Publishing Ltd    

年代:1990

数据来源: WILEY

摘要:

Theintrinsic accuracyof an inductive problem is the accuracy achieved by exhaustive table look‐up. Intrinsic accuracy is the upper bound for any inductive method.Hardconcepts are concepts that have high intrinsic accuracy, but which cannot be learned effectively with traditional inductive methods. To learn hard concepts, we must useconstructive induction ‐methods that create new features. We use measures ofconcept dispersionto explore (conceptually and empirically) the inherent weaknesses of traditional inductive approaches. These structural defects are buried in the design of the algorithms and prevent the learning of hard concepts. After studying some examples of successful and unsuccessful feature construction (“success” being defined here in terms of accuracy), we introduce a single measure of inductive difficulty that we callvariation.We argue for a specific approach to constructive induction that reduces variation by incorporating various kinds of domain knowledge. All of these kinds of domain knowledge boil down toutility invariants,i.e., transformations that group together non‐contiguous portions of feature space having similar class‐membership values. Utility invariants manifest themselves in various ways: in some cases they exist in the user's stock of domain knowledge, in other cases they may be discovered via methods

ISSN:0824-7935    

DOI:10.1111/j.1467-8640.1990.tb00298.x

出版商:Blackwell Publishing Ltd    

年代:1990

数据来源: WILEY