John Gaschnig Department of Computer Science Carnegie-Mellon University Pittsburgh, PA 15213 Abstract Here we describe an approach, based upon a notion of problem similarity, that can be used when attempting to devise a heuristic for a given search problem (of a sort represented by graphs). The next step is to find an algorithm for finding paths in P2, then apply this algorithm in a certain way as a heuristic for P1. Using the As algorithm, we experimentally compare the performance of this "maxsort" heuristic for the 8-puzzle with others in the literature. Many combinatorially large problems cannot be solved feasibly by exhaustive case analysis or brute force search, but can be solved efficiently if a heuristic can be devised to guide the search. Research to date on devising heuristics has spanned several problem-solving domains and several approaches.
A system for learning concept descriptions incrementally is described and illustrated by a series of experiments in the domains of insect classification, chess endgames and plant disease diagnosis. The system employs a full-memory learning method that incrementally improves hypotheses, but does not forget facts. The method is used to form both characteristic descriptions, which describe a concept in detail, and discriminant descriptions, which specify only properties needed to distinguish a given concept from a given set of other concepts. Experimental results show the advantages of inducing and maintaining only characteristic descriptions during learning and creating discriminant descriptions from them when a classification decision is necessary. Research in the area of concept learning from examples has been concerned mainly with methods for single step, or non-incremental, learning.
A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. Here we describe several applications of the PROSPECTOR consultation system to mineral exploration tasks. One was a pilot study conducted for the National Uranium Resource Estimate program of the U.S. Department of Energy. This application estimated the favourability of several test regions for occurrence of sandstone uranium deposits. For credibility, the study was preceded by a performance evaluation of the relevant portion of PROSPECTOR's knowledge base, which showed that PROSPECTOR's conclusions agreed very closely with those of the model designer over a broad range of conditions and levels of detail.
In particular its task domain is the analysis of mass spectra, chemical data gathered routinely from a relatively new analytical instrument, the mass spectrometer. This collaboration of chemists and computer scientists has produced what appears to be an interesting program from the viewpoint of artificial intelligence and a useful tool from the viewpoint of chemistry. For this discussion it is sufficient to say that a mass spectrometer is an instrument into which is put a minute sample of some chemical compound and out of which comes data usually represented as a bar graph. This is what is referred to here as the mass spectrum. The x-points of the bar graph represent the masses of ions produced and the y-points represent the relative abundances of ions of these masses.
This project was supported by the Bureau of Health Services Research and Evaluation, Computer-Based Consultations in Clinical Therapeutics, Research Grant No. HS01544, and by the Veterans Administration. Requests for reprints should be addressed to Ms. A.C. Scott, TC110, Stanford Univ. ABSTRACT The performance of a computer-based clinical consultation system is evaluated. The program, called MYCIN, is designed to function as an aid for infectious disease diagnosis are therapy selection, with an initial emphasis on bacteremias. The evaluation methodology is discussed, as well as the difficulties encountered in attempting to evaluate clinical judgments.
Atm 11177 Stanford Heuristic Programming Project August 1977 Memo HPP-77-25 Computer Science Department Report No. STAN-CS-77-62I THE ART OF ARTIFICIAL INTELLIGENCE: 1. THEMES AND CASE STUDIES OF KNOWLEDGE ENGINEERING by E. A. Feigenbaum COMPUTER SCIENCE DEPARTMENT School of Humanities and Sciences STANFORD UNIVERSITY THE ART OF ARTIFICIAL INTELLIGENCE: I. Themes and Case Studies of Knowledge Engineering STAN-CS-77-621 Heuristic Programming Project Memo 77-25 Edward A. Feigenbaum Department of Computer Science Stanford University Stanford, California ABSTRACT The knowledge engineer practices the art of bringing the principles and tools of Al research to bear on difficult applications problems requiring experts' knowledge for their solution. The technical issues of acquiring this knowledge, representing it, and using it appropriately to construct and explain lines-of-reasoning, are important problems in the design of knowledge-based systems. Various systems that have achieved expert level performance in scientific and medical inference illuminates the art of knowledge engineering and its parent science, Artificial Intelligence. The views and conclusions in this document are those of the author and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research Projects Agency of the United States Government. This research has received support from the lollowing agencies: Defense Advanced Research Projects Agency, DAHC 15-73-C-0435; National Institutes of Health, 5R24-RR00612, RR-00785; National Science Foundation, MCS 76-11649, DCR 74-23461; The Bureau of Health Sciences Research and Evaluation, HS-01544.
A Model For Learning Systems STAN-CS-77-605 Heuristic Programming Project Memo 77-14 Reid G. Smith, Tom M. Mitchell Richard A. Chestek and Bruce G. Buchanan ABSTRACT A model for learnina systems is presented, and representative Al, pattern recognition, and control systems are discussed in terms of its framework. The model details the functional components felt to be essential for any learning system, independent of the techniques used for its construction, and the specific environment In which it operates. These components are performance element, instance selector, critic, learning element, blackboard, and world model. Consideration of learning system design leads naturally to the concept of a layered system, each layer operating at a different level of abstraction. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research ...