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Knowledge-based search tactics are discussed in terms of their role in the functioning of a semantically-based search system for bibliographic information retrieval. This prototype system, EP-X, actively assists users in defining or refining their topics of interest. It does so by applying search tactics, to a knowledge-base describing topics in a particular domain and a database describing the contents of individual documents. arises because: 1. The searcher needs to learn more about the topic area; 2. The searcher is having difficulty expressing her interest clearly; 3. The searcher needs to broaden or narrow her topic because too many or too few documents have been retrieved. Over the past five years, we have been studying ways to assist such information seekers and Krawczak, 1989). This paper summarizes our results and current conclusions about the design of computerized search intermediary systems. Particular attention is given to the idea that, in a semantically-based search system, knowledge-based search tactics can be applied to help the searcher explore a topic area. DEVELOPMENT OF A SEMANTICALLY-BASED SEARCH SYSTEM As part of the initial development of EP-X, we studied intermediaries who were also experienced indexers at Chemical Abstracts Service. Thus, in addition to having experience as intermediaries, they were very knowledgeable about a particular field of chemistry and about the practices and policies followed at Chemical Abstracts when indexing documents in that field. Based on several informal studies of these intermediaries, we concluded that there were four critical aspects of the interactions that we had observed: language fact retrieval, ACM Transactions on Database Systems.M.H. EP-X: A knowledge-based system to aid in searches of the environmental pollution literature.

Knowledge-based search tactics are discussed in terms of their role in the functioning of a semantically-based search system for bibliographic information retrieval. This prototype system, EP-X, actively assists users in defining or refining their topics of interest. It does so by applying search tactics to a knowledge-base describing topics in a particular domain and a database describing the contents of individual documents. This paper reviews the empirical studies that lead to the two central concepts implemented in EP-X: Semantically-based search; Knowledge-based search tactics.

One reason that intelligent tutoring systems (ITSs) are rarely found outside of the research lab has to do with the guidelines available to developers of these systems. First, some of these guidelines are stated as general, abstract goals such as “adapt to the student.” What ITS developers need, however, are specific strategies and techniques which can be implemented in an ITS to accomplish those goals. Second, not all of the guidelines have an empirical basis. One solution to both of these problems is to study human tutors. This paper demonstrates this approach, and discusses an empirical study of human tutors which was conducted to address these issues. Specifically, it discusses 1) the knowledge acquisition method which we designed to capture the appropriate empirical data, 2) how we used this method to study human tutors and students in the medical problem-solving domain of immunohematology (blood banking), 3) several guidelines which appeared to drive the tutors' behavior (e.g., “limit the number of interrupts to the student”), and 4) specific tutoring strategies which can be incorporated into an ITS to make its behavior follow these guidelines.

Investigations of students and practicing medical technologists indicate that both groups make significant numbers of errors on tasks such as antibody identification. One potential solution to help with this problem is to provide access to a computerized learning environment in which users can get exposure to a larger and much broader set of problems than would otherwise be possible. This paper describes such a learning environment, the Transfusion Medicine Tutor, and discusses the ways in which it supports guided discovery learning.