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Footnote

The book does achieve its aim of being a starting point for someone interested in the state of some areas of AI research at the beginning of the new millennium. The book's most irritating feature is the different writing styles of the authors. The book is organized as a collection of papers similar to a typical graduate survey course packet, and as a result the book does not possess a narrative flow. Also the book contains a number of other major weaknesses such as a lack of an introductory or concluding chapter. The book could greatly benefit from an introductory chapter that would introduce readers to the areas of AI, explain why such a book is needed, and explain why each author's research is important. The manner in which the book currently handles these issues is a preface that talks about some of the above issues in a superficial manner. Also such an introductory chapter could be used to expound an what level of AI mathematical and statistical knowledge is expected from readers in order to gain maximum benefit from this book. A concluding chapter would be useful to readers interested in the other areas of AI not covered by the book, as well as open issues common to all of the research presented. In addition, most of the contributors come exclusively from the computer science field, which heavily slants the work toward the computer science community. A great deal of the research presented is being used by a number of research communities outside of computer science, such as biotechnology and information technology. A wider audience for this book could have been achieved by including a more diverse range of authors showing the interdisciplinary nature of many of these fields. Also the book's editors state, "The reader is expected to have basic knowledge of AI at the level of an introductory course to the field" (p vii), which is not the case for this book. Readers need at least a strong familiarity with many of the core concepts within AI, because a number of the chapters are shallow and terse in their historical overviews. Overall, this book would be a useful tool for a professor putting together a survey course an AI research. Most importantly the book would be useful for eager graduate students in need of a starting point for their research for their thesis. This book is best suited as a reference guide to be used by individuals with a strong familiarity with AI."

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Content

National FRBR experiments The larger the bibliographic database, the greater the effect of "FRBR-like" design in reducing the appearance of duplicate records. LC, RLG, and OCLC, all influenced by FRBR, are experimenting with the redesign of their databases. LC's Network Development and MARC Standards Office has posted at its web site the results of some of its investigations into FRBR and MARC, including possible display options for bibliographic information. The design of RLG's public catalog, RedLightGreen, has been described as "FRBR-ish" by Merrilee Proffitt, RLG's program officer. If you try a search for a prolific author or much-published title in RedLightGreen, you'll probably find that the display of search results is much different than what you would expect. OCLC Research has developed a prototype "frbrized" database for fiction, OCLC FictionFinder. Try a title search for a classic title like Romeo and Juliet and observe that OCLC includes, in the initial display of results (described as "works"), a graphic indicator (stars, ranging from one to five). These show in rough terms how many libraries own the work-Romeo and Juliet clearly gets a five. Indicators like this are something resource sharing staff can consider an "ILL quality rating." If you're intrigued by FRBR's possibilities and what they could mean to resource sharing workflow, start talking. Now is the time to connect with colleagues, your local and/or consortial system vendor, RLG, OCLC, and your professional organizations. Have input into how systems develop in the FRBR world."