Incremental learning of independent, overlapping, and graded concept descriptions with an instance-based process framework

Supervised learning algorithms make several simplifying assumptions concerning the characteristics of the concept descriptions to be learned. For example, concepts are often assumed to be (1) defined with respect to the same set of relevant attributes, (2) disjoint in instance space, and (3) have uniform instance distributions. While these assumptions constrain the learning task, they unfortunately limit an algorithm's applicability. We believe that supervised learning algorithms should learn attribute relevancies independently for each concept, allow instances to be members of any subset of concepts, and represent graded concept descriptions. This paper introduces a process framework for instance-based learning algorithms that exploit only specific instance and performance feedback information to guide their concept learning processes. We also introduce Bloom, a specific instantiation of this framework. Bloom is a supervised, incremental, instance-based learning algorithm that learns relative attribute relevancies independently for each concept, allows instances to be members of any subset of concepts, and represents graded concept memberships. We describe empirical evidence to support our claims that Bloom can learn independent, overlapping, and graded concept descriptions

Discovering qualitative empirical laws

In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

The structure and formation of natural categories

Categorization and concept formation are critical activities of intelligence. These processes and the conceptual structures that support them raise important issues at the interface of cognitive psychology and artificial intelligence. The work presumes that advances in these and other areas are best facilitated by research methodologies that reward interdisciplinary interaction. In particular, a computational model is described of concept formation and categorization that exploits a rational analysis of basic level effects by Gluck and Corter. Their work provides a clean prescription of human category preferences that is adapted to the task of concept learning. Also, their analysis was extended to account for typicality and fan effects, and speculate on how the concept formation strategies might be extended to other facets of intelligence, such as problem solving

A general theory of discrimination learning

One important component of learning is the ability to determine the correct conditions under which a rule should be applied. We review a number of systems that discover relevant conditions through a generalization process, and discuss some drawbacks of this approach. We then review an alternative approach to learning through discrimination, in which overly general rules are made more conservative when they lead to errors. Unlike generalization-based programs, a discrimination-based system is able to learn disjunctive rules, discover regularities in errorful data, recover from changes in the environment, and learn useful rules despite incomplete representations. We show how our theory of discrimination learning can be applied to the domains of concept attainment, strategy learning, first language acquisition, and cognitive development. Finally, we evaluate the theory along the dimensions of simplicity, generality, and fertility

A course-oriented intelligent tutoring system with probability assessment

Most Intelligent Tutoring Systems (ITSs) in the past have concentrated on small domains and have been topic-oriented. They have tended to be non-extendable prototypes and have neglected the expertise of human teachers. It is argued here that a promising approach at this time is to design course-oriented ITS shells which are based on the human teacher. Courses using such shells could be used to take some of the load of first-time delivery and assessment from teachers and lecturers, and leave them more time for individual tutoring. [Continues.

Artificial Intelligence and Human Error Prevention: A Computer Aided Decision Making Approach: Technical Report No. 4: Survey and Analysis of Research on Learning Systems from Artificial Intelligence

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryU.S. Department of Transportation / DOT FA79WA-4360 ABFederal Aviation Administratio

An investigation into the application of machine learning in information retrieval

There is an increasing variety of online databases available which are also evergrowing in size. In retrieving information from these sources, it is important not only to have effective and efficient retrieval techniques but also to enable some form of adaptation to users’ specific needs. Frequent users, in particular, should be able to benefit from their high use of the information retrieval system. A machine learning approach can be applied to help the system adapt to users’ specific needs. It is argued that users have a particular context within which their queries are formed. It is likely that consecutive queries for a particular user will be related in that they will be part of the same context. Thus, a context learner is proposed. In this investigation, the context learner is used for enhancing document ordering in partial match systems