Bibliographic Details
| Title: |
Knowledge Acquisition from Structural Descriptions. |
| Language: |
English |
| Authors: |
Hayes-Roth, Frederick, McDermott, John, Rand Corp., Santa Monica, CA. |
| Availability: |
Publications Department, The Rand Corporation, 1700 Main Street, Santa Monica, California 90406 ($5.00) |
| Peer Reviewed: |
N |
| Page Count: |
67 |
| Publication Date: |
1976 |
| Report Number: |
P-59l0 |
| Document Type: |
Reports - Research |
| Descriptors: |
Algorithms, Cognitive Processes, Componential Analysis, Computational Linguistics, Computer Science, Concept Formation, Difficulty Level, Induction, Models, Task Analysis, Teaching Machines |
| Geographic Terms: |
U.S.; California |
| Abstract: |
The learning machine described in this paper acquires concepts representable as conjunctive forms of the predicate calculus and behaviors representable as productions (antecedent-consequent pairs of such conjunctive forms): these concepts and behavior rules are inferred from sequentially presented pairs of examples by an algorithm that is probably effective for a wide variety of problems. A method for inducing knowledge by abstracting such representations from a sequence of training examples is described. The proposed learning method, interference matching, induces abstractions by finding regional properties common to two or more exemplars. Three tasks solved by a program that performs an interference matching algorithm are presented. Several problems concerning the relational representation of examples and the induction of knowledge by interference matching are also discussed. The similarities between this task and other computer science problems are indicated, and directions for future research are considered. (Author/JEG) |
| Journal Code: |
RIEMAY1979 |
| Entry Date: |
1979 |
| Accession Number: |
ED163912 |
| Database: |
ERIC |
