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This report describes research conducted at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract number N00014-75-C-0643., This paper is intended as a brief introduction to several issues concerning semantic categories. These are the everyday, factual groupings of world knowledge according to some similarity in characteristics. Some psychological data concerning the structure, formation, and use of categories is surveyed. Then several psychological models (set-theoretic and network) are considered. Various artificial intelligence representations (concerning the symbol mapping and recognition problems) dealing with similar issues are also reviewed. It is argued that these data and representations approach semantic categories at too abstract a level and a set of guidelines which may be helpful in constructing a microworld are given., MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agency

The computation of immediate texture discrimination involves finding boundaries between regions of differing texture. Various textures are examined to investigate the factors determining discrimination in the limited domain of line-and-point images. Two operators embodying necessary properties are proposed: length and orientation of actual lines and of local virtual lines between terminators. It is conjectured that these are sufficient as well. Relations between this theory and those of Julesz and of Marr are discussed. Supporting psychological evidence is introduced and an implementation strategy outlined.

This research investigated the application of techniques successfully used in previous information retrieval research, to the more challenging area of medical informatics. It was performed on a biomedical document collection testbed CANCERLIT, provided by the National Cancer Institute (NCI), which contains information on all types of cancer therapy. The quality or usefulness of terms suggested by three different thesauri, one based on MeSH terms, one based solely on terms from the document collection, and one based on the Unified Medical Language System UMLS. Metathesaurus, was explored with the ultimate goal of improving CANCERLIT information search and retrieval. Researchers affiliated with the University of Arizona Cancer Center evaluated lists of related terms suggested by different thesauri for 12 different directed searches in the CANCERLIT testbed. The preliminary results indicated that among the thesauri, there were no statistically significant differences in either term recall or precision. Surprisingly, there was almost no overlap of relevant terms suggested by the different thesauri for a given search. This suggests that recall could be significantly improved by using a combined thesaurus approach., Published in Decision Support Systems, v30 p171-186, 2000. Supported in part by NSF, NASA, NLM and NCI.

This paper discusses several data mining algorithms and techniques that we have developed at the University of Arizona Artificial Intelligence Lab. We have implemented these algorithms and techniques into several prototypes, one of which focuses on medical information developed in cooperation with the National Cancer Institute (NCI) and the University of Illinois at Urbana-Champaign. We propose an architecture for medical knowledge information systems that will permit data mining across several medical information sources and discuss a suite of data mining tools that we are developing to assist NCI in improving public access to and use of their existing vast cancer information collections., Published in Artificial Intelligence Review, v13 p437-466, 1999. Supported in part by NSF, NASA, and NCI.