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

Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

From “Oh, OK” to “Ah, yes” to “Aha!”: Hyper-systemizing and the rewards of insight\ud

Hyper-systemizers are individuals displaying an unusually strong bias toward systemizing, i.e. toward explaining events and solving problems by appeal to mechanisms that do not involve intentions or agency. Hyper-systemizing in combination with deficit mentalizing ability typically presents clinically as an autistic spectrum disorder; however, the development of hyper-systemizing in combination with normal-range mentalizing ability is not well characterized. Based on a review and synthesis of clinical, observational, experimental, and neurofunctional studies, it is hypothesized that repeated episodes of insightful problem solving by systemizing result in attentional and motivational sensitization toward further systemizing via progressive and chronic deactivation of the default network. This hypothesis is distinguished from alternatives, and its correlational and causal implications are discussed. Predictions of the default-deactivation model accessible to survey-based instruments, standard cognitive measures and neurofunctional methods are outlined, and evidence pertaining to them considered

Towards a participatory evaluation methodology: the Southern African pilot learning process

This is draft of a paper by Boston University professor Ann Seidma

Understanding of the Mole Concept Achieved by Students in a Constructivist General Chemistry Course

The purpose of this research project was to study the conceptual understanding achieved in a general chemistry course based on a constructivist approach. A group of 28 students participated in repeated measures obtained by means of conceptual maps about the mole concept prepared three times during the course: at the beginning the course, immediately after the concept was studied, and after studying other related concepts. In addition, eight students selected from the group of 28 were interviewed. The interviews were carried out focusing on their conceptual maps. The analysis of the repeated measures indicated significant differences among the three times, especially between the first two. It was evidenced, therefore, that these students obtained a significantly higher level of understanding of the mole concept. The qualitative analysis carried out with students identified a broad range of responses that represent different levels of hierarchical organization, of progressive differentiation, and of formation of significant relations of the mole concept. Some recommendations offered are to develop and implement teaching methods that promote understanding of scientific concepts, and to prepare science professors and teachers to emphasize teaching for conceptual understanding

Sex differences in mathematical performance : what do we know about them?

It is generally accepted that mathematics is one academic field where male superiority of achievement is well-established. Far fewer women than men go into careers as mathematicians and those who do, generally do not reach equal employment status with men. Such facts seem to be the culmination of sex differences in mathematical performance which begin to appear around the age of 12 to 15. Up till then, the mathematical performance of boys and girls seems to be fairly equal at any of the three cognitive levels of computation, knowledge of concepts and problem-solving ability on which mathematical achievement is most common:y gauged. The change in performance at the secondary level of schooling tends to be in favour of males who are seen to perform better than females particularly on tasks involving visual spatialisation ability and mathematical reasoning. The girls' discontinuity of performance, even when they have had an identical learning background, has prompted researchers to investigate possible explanations for a phenomenon which Walden and Walkerdine (1982) stress should not be confused with an "overall failure."peer-reviewe