Abstract: A major impediment to the development of high-performance knowledge-based systems arises from the prohibitive effort involved in equipping these systems with a sufficient set of problem-solving methods. Thus, one important research problem in Machine Learning has been the study of techniques for inferring problem-solving methods from examples. Although a number of techniques for learning problem-solving methods have been described in the literature, all of them assume a state-space model of problem-solving. In this paper we describe a new technique for learning problem-reduction methods, Verification-Based Learning (VBL), which extends the earlier techniques to the problem-reduction formulation of problem-solving. We illustrate the VBL technique with examples drawn from circuit design and symbolic integration.

Abstract: The system consists of three steps. At the very outset some pre-processing are applied on the input image. Secondly face features are extracted, which will be taken as the input of the Back-propagation Neural Network (BPN) and Genetic Algorithm (GA) in the third step and classification is carried out by using BPN and GA. The proposed approaches are tested on a number of face images. Experimental results demonstrate the higher degree performance of these algorithms.

Abstract: The purpose of this study was to investigate three instructional design variables hypothesized to improve rule learning by use of information processing methods. These variables included: analysis and structure of information, response-sensitive sequencing of information, and monitoring of learning time. Using secondary education students learning internal punctuation rules, results from two experiments showed that (a) structuring information by a schematic analysis improved learning over a taxonomic analysis, (b) a response-sensitive sequence that first adapted instruction for generalization and second discrimination improved learning over either sequence separately, and (c) program monitoring of the display time interval improved learning over learner control. Findings are discussed in reference to an interactive nature of learning theory, instructional systems, and computer technology.

Abstract: A class of learning tasks is described that combines aspects of learning automation tasks and supervised learning pattern-classification tasks. These tasks are called associative reinforcement learning tasks. An algorithm is presented, called the associative reward-penalty, or AR-P algorithm for which a form of optimal performance is proved. This algorithm simultaneously generalizes a class of stochastic learning automata and a class of supervised learning pattern-classification methods related to the Robbins-Monro stochastic approximation procedure. The relevance of this hybrid algorithm is discussed with respect to the collective behaviour of learning automata and the behaviour of networks of pattern-classifying adaptive elements. Simulation results are presented that illustrate the associative reinforcement learning task and the performance of the AR-P algorithm as compared with that of several existing algorithms.