ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS RESEARCH AND THEIR POSSIBLE IMPACT ON INFORMATION SCIENCE EDUCATION

Applications from artificial intelligence and expert systems research will most likely be incorporated in future information services to improve decision making, to solve problems normally thought to require human intelligence, and to achieve levels of performance previously obtainable only by human experts. Expert systems are creating new methods of replicating and multiplying the value of human expertise while preserving this knowledge in computer storage. The successes achieved by these early systems have been modest, but they hold great promise, and they are attracting widespread interest. Information scientists are exploring potential applications of artificial intelligence as guidance for the development of new information services such as aids in formulating search requests, as general problem treatment schemes, and as expert systems for library management, cataloguing, and reference. The role of the library is changing, and schools need to modify their programmes to prepare students for new roles and new careers in information transfer.

[1]  Nicholas J. Belkin,et al.  Distributed expert problem treatment as a model for information system analysis and design , 1982 .

[2]  Harold Borko,et al.  The construction of an empirically based mathematically derived classification system , 1899, AIEE-IRE '62 (Spring).

[3]  Linda C. Smith.  Artificial intelligence in information retrieval systems , 1976, Inf. Process. Manag..

[4]  Hans Peter Luhn,et al.  A Statistical Approach to Mechanized Encoding and Searching of Literary Information , 1957, IBM J. Res. Dev..

[5]  Arthur S. Pollitt.  An expert system as an online search intermediary , 1981 .

[6]  Blaise Cronin,et al.  Expert systems and library/information work , 1983 .

[7]  Kevin P. Jones.  The effects of Expert and Allied Systems on information handling: some scenarios , 1984 .

[8]  Brian James,et al.  Towards an expert system for cataloguing: some experiments based on AACR2 , 1984 .

[9]  Kathleen T. Bivins Noerr,et al.  A microcomputer alternative for information handling: REFLES , 1981, Inf. Process. Manag..

[10]  William S. Cooper,et al.  Foundations of Probabilistic and Utility-Theoretic Indexing , 1978, JACM.

[11]  Donald E. Walker,et al.  The organization and use of information: Contributions of information science, computational linguistics and artificial intelligence , 1981, J. Am. Soc. Inf. Sci..

[12]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[13]  Mark SPalay A J Fox.  The BROWSE system: an introduction , 1979 .

[14]  Allen Newell,et al.  Intellectual issues in the history of artificial intelligence , 1983 .

[15]  William J. Clancey,et al.  The Epistemology of a Rule-Based Expert System - A Framework for Explanation , 1981, Artif. Intell..

[16]  D. Michie,et al.  Expert systems in business , 1983 .

[17]  van Rijsbergen,et al.  Automatic Classification in Information Retrieval. , 1978 .

[18]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part I. Background and Theory , 1997, J. Documentation.

[19]  R. Duda,et al.  Expert Systems Research. , 1983, Science.

[20]  Stephen P. Harter,et al.  Statistical Approaches to Automatic Indexing. , 1978 .

[21]  T. E. Doszkocs,et al.  Searching MEDLINE in English: a prototype user interface with natural language query, ranked output, and relevance feedback , 1979 .