Machine learning: a guide to current research
Machine learning: a guide to current research
Learning and reasoning by analogy
Communications of the ACM
Explanation-Based Learning: An Alternative View
Machine Learning
Learning Expert Knowledge and Theorem Proving
GWAI-86 und 2. Österreichische Artificial-Intelligence-Tagung
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
| Hi-index | 0.00 |
The paper presents an interactive approach to learning apprentice systems for weak theory domains. The approach consists of a combination of teaming by analogy and learning by generalizing instances. One main point of this approach is that it uses the explanations drawn from an example, both to reduce the version space of me rules to be learned, and to generate new examples, analogous to the given one. Another important point is that it demonstrates not only that over-generalization is harmless but also useful and necessary, when interacting with a user. It allows to use the theory of the domain, though incomplete as it is, in order to extract the missing knowledge by asking "clever" questions to its user. This paper presents a first prototypical version of DISCIPLE and its use to the design of technologies for the manufacturing of loudspeakers.