Inductive Learning
Quantifying the Inductive Bias in Concept Learning
See also: 2005 AAAI Classic Paper Awards summary of significance by Tom Mitchell in AI Magazine 26(4), 2005.Proc. AAAI-86
Constraint-based generalization: Learning game-playing plans from single examples
In AAAI-84, pp. 251â254.
Generalization as Search
"The purpose of this paper is to compare various approaches to generalization in terms of a single framework. Toward this end, generalization is cast as a search problem, and alternative methods for generalization are characterized in terms of the search strategies that they employ. This characterization uncovers similarities among approaches, and leads to a comparison of relative capabilities and computational complexities of alternative approaches. The characterization allows a precise comparison of systems that utilize different representations for learned generalizations."Artificial Intelligence, 18 (2), 203-26.
Models of learning systems
Buchanan, B. G. | Mitchell, T. M. | Smith, R. G. | Johnson, C. R.
"The terms adaptation, learning, concept-formation, induction, self-organization, and self-repair have all been used in the context of learning system (LS) research. The research has been conducted within many different scientific communities, however, and these terms have come to have a variety of meanings. It is therefore often difficult to recognize that problems which are described differently may in fact be identical. Learning system models as well are often tuned to the require- ments of a particular discipline and are not suitable for application in related disciplines."In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker
Version spaces: A candidate elmination approach to rule learning
"An important research problem in artificial intelligence is the study of methods for learning general concepts or rules from a set of training instances. An approach to this problem is presented which is guaranteed to find, without backtracing, all rule versions consistent with a set of positive and negative training instances. The algorithm put forth uses a representation of the space of those rules consistent with the observed training data. This "rule version space" is modified in response to new training instances by eliminating candidate rule versions found to conflict with each new instance. The use of version spaces is discussed in the context of Meta-DENDRAL, a program which learns rules in the domain of chemical spectroscopy."Proc. IJCAI 77 VOL 1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY CAMBRIDGE, MASSACHUSETTS, USA AUGUST 22 - 25 , 1977, pp.305-310
An experiment on inductive learning in chess end games.
In E. W. Elcock and D. Michie (Eds.), Machine intelligence 8. New York: Wiley.
Generalization learning techniques for automating the learning of heuristics
This paper investigates the problem of implementing machine learning of heuristics. First, a method of representing heuristics as production rules is developed which facilitates dynamic manipulation of the heuristics by the program embodying them. Second, procedures are developed which permit a problem-solving program employing heuristics in production rule form to learn to improve its performance by evaluating and modifying existing heuristics and hypothesizing new ones, either during an explicit training process or during normal program operation. Third, the feasibility of these ideas in a complex problem-solving situation is demonstrated by using them in a program to make the bet decision in draw poker. Finally, problems which merit further investigation are discussed, including the problem of defining the task environment and the problem of adapting the system to board games.