Collaborating Authors

Information Technology

An Overview of Meta-Level Architecture


"One of the biggest problems in AT programming is the difficulty of specifying control. Meta-level architecture is a knowledge engineering approach to coping with this difficulty. The key feature of the architecture is a declarative control language that allows one to write partial specifications of program behavior. This flexibility facilitates incremental system dcvclopment and the integration of disparate architectures like demons, object-oriented programming, and controlled deduction. This paper presents the language, describes an appropriate, and cliscusses the issues of compiling. It illustrales the architecture with a variety of examples and reports some experience in using the architecture in building expert systems."Earlier: M. Genesereth and D.E. Smith. Meta-level Architecture. Memo HPP-81-6, Computer Science Department, Stanford University, 1981.In Proceedings of the AAAI, Washington, DC., August, 1983

Criteria for representations of shape


In J. Beck, B. Hope, and A. Rosenfeld (Eds.), Human and machine vision. New York: Academic Press

A deductive model of belief


Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Karlsruhe, West Germany, 377-381

Interviewer/Reasoner Model: An Approach to Improving System Responsiveness in Interactive AI Systems

AI Magazine

Interactive intelligent systems often suffer from a basic conflict between their computationally intensive nature and the need for responsiveness to a user. This paper introduces the Interviewer/Reasoner model, which helps to reduce this conflict. The Interviewer's primary function is to gather data while providing an acceptable response time to the user. The Reasoner does most of the symbolic computation for the system.

An Approach to Verifying Completeness and Consistency in a Rule-Based Expert System

AI Magazine

We describe a program for verifying that a set of rules in an expert system comprehensively spans the knowledge of a specialized domain. The program has been devised and tested within the context of the ONCOCIN System, a rule-based consultant for clinical oncology. The stylized format of ONCOIN's rule has allowed the automatic detection of a number of common errors as the knowledge base has been developed. This capability suggests a general mechanism for correcting many problems with knowledge base completeness and consistency before they can cause performance errors.

Why People Think Computers Can't

AI Magazine

Today, surrounded by so many automatic machines industrial robots, and the R2-D2's of Star wars movies, most people think AI is much more advanced than it is. But still, many "computer experts" don't believe that machines will ever "really think." I think those specialists are too used to explaining that there's nothing inside computers but little electric currents. And there are many other reasons why so many experts still maintain that machines can never be creative, intuitive, or emotional, and will never really think, believe, or understand anything.