Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Intelligent tutoring systems: an overview

Abstract: In this paper we look at the evolutionary development of Computer Assisted Instruction from the early days of ‘linear programs’ up to the use of ‘expert systems’ in education and training. We present the basic principles of Intelligent Tutoring Systems (ITS) which are capable of rich interaction with the student, which know how to teach, and who and what they are teaching. We point out the need for knowledge representation formalisms which can support ITS and present one such formalism (production systems). In the framework presented we describe systems developed for the teaching of modern languages, electronic trouble shooting and computer programming. Finally we point out the shortcomings of ITS and identify areas where a consensus of opinion does not exist. © 1986 Wiley Periodicals, Inc. (Masoud Yazdani: Masoud Yazdani studied Computer Science at the University of Essex and Artificial Intelligence at the University of Sussex. He joined Exeter University in 1980 as a lecturer in Computer Science. His research work is in two areas of computational models of creativity and intelligent tutoring systems. This latter area has led to implementation of various systems using artificial intelligence techniques to teach arithmetic and foreign languages. He is Chairman of Intellect Limited, a company specialising in applications of AI and to educate industry in what AI can offer it. He has acted as a consultant to various industrial companies including Acorn Computers, GTE Inc. and ITT Engineering Support Centre. He is the Committee Secretary for the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB). His books include New Horizons in Educational Computing, Artificial Intelligence: Human Effects, both published by Ellis Horwood Ltd. and Artificial Intelligence: Principles and Applications published by Chapman and Hall.)

DAML+OIL: A Reason-Able Web Ontology Language

Abstract: Ontologies are set to play a key role in the "Semantic Web", extending syntactic interoperability to semantic interoperability by providing a source of shared and precisely defined terms. DAML+OIL is an ontology language specifically designed for use on the Web; it exploits existing Web standards (XML and RDF), adding the familiar ontological primitives of object oriented and frame based systems, and the formal rigor of a very expressive description logic. The logical basis of the language means that reasoning services can be provided, both to support ontology design and to make DAML+OIL described Web resources more accessible to automated processes.

Trust based recommender system for the semantic web

Abstract: This paper proposes the design of a recommender system that uses knowledge stored in the form of ontologies. The interactions amongst the peer agents for generating recommendations are based on the trust network that exists between them. Recommendations about a product given by peer agents are in the form of Intuitionistic Fuzzy Sets specified using degree of membership, non membership and uncertainty. In literature, the recommender systems use databases to generate recommendations. The presented design uses ontologies, a knowledge representation technique for creating annotated content for Semantic Web. Seeing the potential and popularity of ontologies among researchers, we believe that ontologies will be build and maintained in numerous knowledge domains for the Semantic Web and future applications. The presented recommender system uses temporal ontologies that absorb the effect of changes in the ontologies due to the dynamic nature of domains, in addition to the benefits of ontologies. A case study of tourism recommender system is chosen to generate the recommendations for the selection of destination, travel agents and the flight schedule. A comparison of the generated recommendations with the manual recommendations by peers establishes the validity of the presented recommender system.

Evolution of data modeling for databases

Abstract: The focus has subsequently shifted to modeling data as seen by the application and the user. Basic data abstraction concepts of classification , generalization, aggregation, and identification were applied in different combinations and different degrees to produce a plethora of \"semantic\" data models in the late 1970s and early 1980s. This article traces this evolution of data models and discusses the recent developments that have dominated the commercial practice of data modeling: the entity-relationship, the functional, and the object-oriented approaches. The article concludes with an overview of the current areas such as modeling of dynamic, active databases, and knowledge discovery from databases. Data modeling benefited immensely from developments in knowledge representation and ter-minological reasoning, and new models such as CANDIDE [10] are springing up as a result of the marriage between these two areas. Certain developments have dominated the commercial practice of data *The word Data will be used in singular throughout this article in keeping with the convention in database literature. modeling: the entity-relationship [14], and the binary approach called NIAM [37] are two examples. The functional approach was exemplified by the DAPLEX model [34] and is having an impact on object models coupled with functions such as the Iris model, now available commercially from Hewlett-Packard in their Open ODB system. A variant of the ER model called IDEF/1X (see [25]) gained a good degree of following in some government establishments. Recently, a major effort for standardizing the representation and modeling of parts data and designs under the name Product Data Exchange using STEP (PDES) [32] is under way. STEP is the ISO standard for the Exchange of Product model data. This has brought about the renaissance of data modeling which is being applied in diverse industries such as building and construction, electrical components, and architecture. Thus, the scope of data modeling now extends far beyond what it was in the early days of file-oriented systems; many organizations are embarking on corporate data modeling as a part of the strategic planning activity. Since this issue of Communications is devoted to different aspects of modeling that include object-oriented analysis and modeling as well as the knowledge representation area, we will not dwell heavily on it. Our focus will be on the mod-eling of data as applied to the design of database structures. We will highlight the current trends in object oriented modeling of data as well as modeling of active …

Computer Automated Multi-Paradigm Modeling: An Introduction

Abstract: Modeling and simulation are quickly becoming the primary enablers for complex system design. They allow the representation of intricate knowledge at various levels of abstraction and allow automated analysis as well as synthesis. The heterogeneity of the design process, as much as of the system itself, however, requires a manifold of formalisms tailored to the specific task at hand. Efficient design approaches aim to combine different models of a system under study and maximally use the knowledge captured in them. Computer Automated Multi-Paradigm Modeling (CAMPaM) is the emerging field that addresses the issues involved and formulates a domain-independent framework along three dimensions: (1) multiple levels of abstraction, (2) multiformalism modeling, and (3) meta-modeling. This article presents an overview of the CAMPaM field and shows how transformations assume a central place. These transformation are, in turn, explicitly modeled themselves by graph grammars.