Any learning process can be viewed as a self-modification of the leaxnefs current knowledge tArough an. interaction with some information source. Such knowledge modification is guided by the learner's deshe to achieve a certain outcome, and can engage any kind of inference. The type of inference involved depends on he input information, the current (background) knowledge and the learneFs task ax hand. Based on such a view of learning, several fundamental concepts are analized and clarified, in paxticular, analytic and synthetic learning, derivm:ional and hypothetical explanation, constnictive induction, abduction, abstraction and deductive generalization. It is shown that inductive generalization and abduction can be viewed as two basic forms of general induction, and that abstraction and deductive generalization axe two related forms of constructive deduction. Using this conceptual framework, a methodology for multistrategy task-adaptive learning (MTL) is outlined, in which learning strategies axe combined dynamically, depending on the current learning situation. Speccally, an MTL learner anaLizes a "wiad" relationship among the input information, the background knowledge and the learning task, and on that basis determines which strategy, or. a combination thereof, is most appropriate at a given learning step. To implement the MTL methodology, a new knowledge representation is proposed, based on the parametric association rules (PARs). Basic ideas of MTL are illustrated by means of the well-known "cup" example, through which is shown how an MTL learner can employ, depending the above mad relationship, emprical learning, constructive inductive generalization, abduction, explanation-based learning and absuaction.

An enormous proliferation of databases in almost every area of human endeavor has created a great demand for new, powerful tools for turning data into useful, task-oriented knowledge. In efforts to satisfy this need, researchers have been exploring ideas and methods developed in machine learning, pattern recognition, statistical data analysis, data visualization, neural nets, etc. These efforts have led to the emergence of a new research area, frequently called data mining and knowledge discovery. The first part of this chapter is a compendium of ideas on the applicability of symbolic machine learning methods to this area. The second part describes a multistrategy methodology for conceptual data exploration, by which we mean the derivation of high-level concepts and descriptions from data through symbolic reasoning involving both data and background knowledge. The methodology, which has been implemented in the INLEN system, combines machine learning, database and knowledge-based techn...

This paper presents a method for constructive induction in which new problem-relevant atu'ibutes are generated by analyzing iteratively created inductive hypotheses. The method starts by creating a set of rules from given examples using the AQ algorithm. These rules are then evaluated according to a "rule quality criterion." Subsets of the best-performing rules for each decision class are selected to form new attributes. These new attributes are used to reformulate the training examples used in the previous step, and the whole inductive process starts again. This iterative process ends when the performance of the rules exceeds a determined threshold. In the experiments on learning different DNF functions, the method outperformed in terms of predictive accuracy both, the AQ15 role learning method, as well as the REEDWOOD decision tree learning method.

EMERALD 1 is a large-scale system integrating several advanced programs exhibiting different forms of learning or discovery. The system is intended to support teaching and research in the area of machine learning. It enables a user to experiment with the individual programs, run them on various problems, and test the performance of the programs. The problems are defined by a user from a set of predefined visual objects, displayed through color graphics facilities. The current version of the system incorporates the following programs, each displaying the capacity for some simple form of learning or discovery: AQ - learns general rules from examples of correct or incorrect decisions made by experts.

The paper presents an approach to the acquisition of texture models of specific objects under the following assumptions: (I) the system has to recognize objects on a sequence of images. (2) images of a sequence demonstrate the variability of conditions under which objects are perceived (e.g., resolution. lighting, surface positioning), (3) an observer or objects can move, (4) the extraction of texture attributes and training events can be imperfect, and (5) the system has to work autonomously (Le. • without teacher help). In order to recognize textured objects under such assumptions. the system has to adapt to the environment through the evolution of texture models. We propose to apply an incrementalleaming methodology to acquire texture descriptions from a sequence of images. The closed-loop system architecture integrates recognition and leaming processes allowing the system to evolve texture models. While the initial acquisition of texture models is driven by a teacher. the evolution of these models is performed over a sequence of images without teacher help. The texture descriptions initially acquired are applied to recognizing and to extracting objects on the next images. The effectiveness of such recognition

Introduction Cognitive scientists have been, for years, searching for essential ingredients of intelligence. Although this issue may not be satisfactorily resolved for quite some time, two abilities are clearly central to intelligent behaviour. One is the ability to acquire knowledge or skill through experience; that is, the ability to learn. The second is the ability to apply the knowledge or skill possessed to solve new problems; that is, the ability to reason. The new problems may concern actual events in the real world: for example, when one has to react to a new external stimulus; or may be imaginary, for instance, when one creates them for planning purposes. A precondition for the above abilities is the capability to represent diverse forms of knowledge. As our knowledge is built of individual concepts, to represent knowledge one needs to represent concepts. Consequently, understanding how concepts are represented is a fundamental problem underlying all efforts in t

Any learning process can be viewed as a self-modification of the leamer's current knowledge through an interaction with some information source. Such knowledge modification s graded by the learner s destre to achieve a certain outcome, and can engage any kind of inference. The typ0 of inference involved depends on the input information, the current (background) knowledge and the learne's task,.at h, and: Based on such a view of learning, several fundamental concepts are ananzeu ano clarified, in particular, analytic and synthetic learning, derivational and hypothetical explanation, constructive induction, abduction, abstraction and deductive generalization. It is shown that inductive generalization and abduction can be viewed as two basic forms of general induction, and that abstraction and deductive generalization are two related forms of constructive deduction. Using this conceptual framework, a methodology for multistrategy task-adaptive learning (MTL) is outlined, in which learning strategies are combined dynamically, depending on the current learning situation. Specifically, an MTL learner anali?es a "triad" relationship among the input information, the background knowledge and the learning task, and on that basis determines which strategy, or a combination thereof, is most appropriate at a given learning step. To implement the MTL methodology, a new knowledge representation is proposed, based on the parametric association rules (PARs). Basic ideas of MTL are illustrated by means of the well-known "cup" example, through which is shown how an MTL leamer can employ, depending on the above triad relationship, emprical learning, constructive inductive generalization, abduction, explanation-based learning and abstraction.

The paper presents initial results from an emerging new direction in engineering design research, in particular, creative design. It argues that constructive induction, which was originally proposed in the field of machine learning, can serve as a foundation for developing a computational theory of engineering design and design creativity. Constructive induction is a process of creating new knowledge (e.g., design knowledge) by performing two intertwined searches, one---for he most adcquale knowledge representation space, and second---for the best hypothesis in this space. Basic concepts and methods of constructive induction are reviewed and illustrated by examples of heir application to conceptual stxuctural design. Several crucial design concepts, including those of an emergent concept and of a goal-oriented Uausformation of he design represenlation space are interpreted in terms of a construction induction process. It is also shown how constructive induction applies to the conxol of the design creativity level. Several measures of the design complexity and relative creativity are proposed. The conclusion presents some lved problems and a plan for future research.

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With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was developed to provide hands-on experience and an interactive demonstration of several machine learning and discovery capabilities for students in AI and cognitive science, and for AI professionals. The current version of EMERALD integrates five programs that exhibit different types of machine learning and discovery: learning rules from examples, determining structural descriptions of object classes, inventing conceptual clusterings of entities, predicting sequences of objects, and discovering equations characterizing collections of quantitative and qualitative data. EMERALD extensively uses color graphic capabilities, voice synthesis, and a natural language representation of the knowledge acquired by the learning programs. Each program is presented as a "learning robot," which has its own "personali...