A general method is developed to generate fuzzy rules from numerical data. This new method consists of five steps: Step 1 divides the input and output spaces of the given numerical data into fuzzy regions; Step 2 generates fuzzy rules from the given data; Step 3 assigns a degree of each of the generated rules for the purpose of resolving conflicts among the generated rules; Step 4 creates a com...

This paper describes a program that learns procedures b\ examining worked-out examples in a textbook and bv working problems I wo kinds of production (If-then) rules are created: working forward rules that produce an action when a proceduie is executed and difference rules that suggest operators from observed transformations. Dining example learning, the program examines two states in an exampl...

STRUCT is a system that learns structural decision trees from positive and negative examples. The algorithm uses a modification of Pagallo and Haussler's FRINGE algorithm to construct new features in a first-order representation. Experiments compare the effects of different hypothesis evaluation strategies, domain representation, and feature construction. STRUCT is also compared with Quinlan's ...

This paper presents a new machine learning system called SHAPE. The input data are vectors of properties (represented as attribute-value pairs) which are used to describe individual cases, examples or observations in a given world. Each case belongs to exactly one of a set of classes, and the aim is to produce a collection of decision rules concluding the class according to the properties obser...

This paper addresses the problem of learning the best approximation of a concept from examples, when the concept cannot be expressed in the learner’s representation language. It presents a method that determines the version space of the best approximations and demonstrates that for any given approximation of the target concept there is a better approximation in this version space. The method do...

Physically based vibration modes have been shown to provide a useful mechanism for describing non-rigid motions of articulated and deformable objects. The approach relies on assumptions being made about the elastic properties of an object to generate a compact set of orthogonal shape parameters which can then be used for tracking and data approximation. We present a method for automatically gen...

The task of inductive learning from examples places constraints on the representation of training instances and concepts. These constraints are different from, and often incompatible with, the constraints placed on the representation by the performance task. This incompatibility explains why previous researchers have found it so difficult to construct good representations for inductive learning...

Introduction Recent work in skill acquisition has suggested that the use of examples of previously attempted problems is critical during the early stages of skill learning and is still common after a skill has been acquired (Pirolli & Anderson, 1985; Ross & Kennedy 1990). Much of the data on the effects of specific prior problems has been published in the crossdomain analogy literature where ba...

In this paper, we present a multi-pronged approach to the “Learning from Example” problem. In particular, we present a framework for integrating learning into a standard, hybrid navigation strategy, composed of both plan-based and reactive controllers. Based on the classification of colors and textures as either good or bad, a global map is populated with estimates of preferability in conjuncti...

Much human learning of mathematics takes place from worked examples, yet this is a subject that has not received much study in Cognitive Science. A major problem is to ensure that a model can learn in a general way, and not be limited to a small subset of mathematics, such as Calculus. This paper describes a computational model of how students learn problem solving heuristics. The model is impl...