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this paper advocates the development of methods for conceplual data analysis. Such methods aim at semi-automating the processes of determining high-level data interpretations, and discovering qualitative patterns in data. It is argued that these methods could be built on the basis of algorithms developed in the area of machine learning. An exemplary system utilizing such algorithms, INLEN, is discussed. The system integrates machine learning and statistical analysis techniques with database and expert system technologies. Selected capabilities of the system are illustrated by examples from implemented modules

We propose a solution to problem solving by analogy which is an alternative to Carbonell's transformational analogy. Given a plan that succeeds for the base, we apply the plan to the target and propose to correct its failures by an abductive recovery mechanism inspired from abductive recovery from failed proofs. 1 Introduction The analogy scheme we shall use in this paper is quite a classical one (Winston, 1982; Gentner, 1983; Chouraqui, 1985; Falkenhainer, Forbus, and Gentner, 1986; Carbonell, 1983, 1986; Kedar-Cabelli, 1988; Kodratoff, 1988). It can be described as follows. Let us suppose that we dispose of a piece of information, the base, that can be put into the form of a doublet (A, B) in which it is known that B depends on A. This dependency will often be causal, and it does not need to be very formal nor strict. In the following, we shall call this relation b, and refer to it as the causality 1 of the analogy. Suppose now that we find an other piece of information, th...

This dissertation may be made available for consultation within the University Library and may be photocopied or lent to other libraries for the purposes of consultation. Signed:..................................................... Learning Classifier Systems (Holland 1978) are a Machine Learning technique in which a set of simplified production rules are discovered to solve a given problem. The system must identify patterns within the data it receives from the problem environment, enabling it to classify other, previously unseen inputs. The rules, known as classifiers, are selected and manipulated using a Genetic Algorithm (Holland 1975). This dissertation focuses on Pittsburgh approach classifier systems (Smith 1980), where many candidate rule sets compete with one another. Such systems tend to be slow since the Genetic Algorithm must work with large structures, namely entire sets of classifiers; increasing their efficiency is therefore an important research topic. We propose a technique in which the rule sets are compressed before manipulation and then re-expanded using principles from Grammatical

. Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptative target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented and its strong and weak points will be underlined. In conclusion we will suggest what could be the "ideal" learning system for vision. 1. Introduction An Image Understanding System aims at providing a semantic description of images by interpreting low level information with the help of background knowledge about images a...