Chandrasekaran, B.



The Dark Ages of AI: A Panel Discussion at AAAI-84

AI Magazine

This panel, which met in Austin, Texas, discussed the "deep unease among AI researchers who have been around more than the last four years or so ... that perhaps expectations about AI are too high, and that this will eventually result in disaster."


Tenth Annual Workshop on Artificial Intelligence in Medicine: An Overview

AI Magazine

The Artificial Intelligence in Medicine (AIM) Workshop has become a tradition. Meeting every year for the past nine years, it has been the forum where all the issues from basic research through applications to implementations have been discussed; it has also become a community building activity, bringing together researchers, medical practitioners, and government and industry sponsors of AIM activities. The AIM Workshop held at Fawcett Center for Tomorrow at Ohio State University, June 30 - July 3, 1984, was no exception. It brought together more than 100 active participants in AIM.


Artificial Intelligence Research at The Ohio State University

AI Magazine

The AI Group at The Ohio State University conducts a broad range of research projects in knowledge-based reasoning. The primary focus of this work is on analyzing problem solving, especially within knowledge -rich domains.


On Evaluating Artificial Intelligence Systems for Medical Diagnosis

AI Magazine

Among the difficulties in evaluating AI-type medical diagnosis systems are: the intermediate conclusions of the AI system need to be looked at in addition to the "final " answer; the "superhuman human" fallacy must be guarded against; and methods for estimating how the approach will scale upwards to larger domains are needed. We propose to measure both the accuracy of diagnosis and the structure of reasoning, the latter with a view to gauging how well the system will scale up.


Towards a Taxonomy of Problem Solving Types

AI Magazine

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.