J
John McDermott
Researcher at Carnegie Mellon University
Publications - 69
Citations - 7783
John McDermott is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Knowledge acquisition & Expert system. The author has an hindex of 28, co-authored 68 publications receiving 7689 citations.
Papers
More filters
Journal ArticleDOI
Expert and novice performance in solving physics problems
TL;DR: Although a sizable body of knowledge is prerequisite to expert skill, that knowledge must be indexed by large numbers of patterns that, on recognition, guide the expert in a fraction of a second to relevant parts of the knowledge store.
Journal ArticleDOI
R1: a rule-based configurer of computer systems
TL;DR: R1 is a program that configures VAX-11/780 computer systems and uses Match as its principal problem solving method; it has sufficient knowledge of the configuration domain and of the peculiarities of the various configuration constraints that at each step in the configuration process, it simply recognizes what to do.
Journal ArticleDOI
Models of Competence in Solving Physics Problems
TL;DR: A set of two computer-implemented models that solve physics problems in ways characteristic of more and less competent human solvers are described, providing a good account of the order in which principles are applied by humansolvers working problems in kinematics and dynamics.
Journal ArticleDOI
Experience with a learning personal assistant
TL;DR: The design of one particular learning assistant is described: a calendar manager, called CAP (Calendar APprentice), that learns user scheduling preferences from experience and suggests that machine learning methods may play an important role in future personal software assistants.
Journal ArticleDOI
Rule-Based Interpretation of Aerial Imagery
TL;DR: In this article, the authors describe the organization of a rule-based system, SPAM, that uses map and domain-specific knowledge to interpret airport scenes, and the results of the system's analysis are characterized by the labeling of individual regions in the image and the collection of these regions into consistent interpretations of the major components of an airport model.