| Title: |
Matters Horn and Other Features in the Computational Learning Theory Landscape: The Notion of Membership. |
| Language: |
English |
| Authors: |
Frazier, Michael Duane; Illinois Univ., Urbana. Dept. of Computer Science. |
| Peer Reviewed: |
N |
| Page Count: |
188 |
| Publication Date: |
1994 |
| Document Type: |
Dissertations/Theses - Doctoral Dissertations; Reports - Evaluative |
| Descriptors: |
Algorithms; Automation; Coding; Computation; Data Collection; Group Membership; Learning Theories; Problem Solving |
| Abstract: |
Computer task automation is part of the natural progression of encoding information. This thesis considers the automation process to be a question of whether it is possible to automatically learn the encoding based on the behavior of the system to be described. A variety of representation languages are considered, as are means for the learner to acquire a variety of types of data about the system in question. The learning process is abstracted as a learning problem in which the goal is to efficiently collect sufficient information to identify some hidden concept using a particular language. The source of information about the concept is its relationship to some class of examples that is assumed to be reasonably available even if the concept is not. The goal of inquiry is to produce a learning algorithm that automates encoding of any representation (or to show that none is possible). It is argued that learning algorithms exist for two natural representation languages: propositional Horn sentences and the CLASSIC description logic, a natural first-order class used in the knowledge representation community. A new method is introduced for modeling uncertainty in the information being collected. Tools that have been developed in computational learning theory can be used for automation in real world tasks outside learning theory. Twenty-two figures are included. (Contains 101 references.) (Author/SLD) |
| Entry Date: |
1995 |
| Accession Number: |
ED377819 |
| Database: |
ERIC |