A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

Humanoid Theory Grounding

In this paper we consider the importance of using a humanoid physical form for a certain proposed kind of robotics, that of theory grounding. Theory grounding involves grounding the theory skills and knowledge of an embodied artificially intelligent (AI) system by developing theory skills and knowledge from the bottom up. Theory grounding can potentially occur in a variety of domains, and the particular domain considered here is that of language. Language is taken to be another “problem space” in which a system can explore and discover solutions. We argue that because theory grounding necessitates robots experiencing domain information, certain behavioral-form aspects, such as abilities to socially smile, point, follow gaze, and generate manual gestures, are necessary for robots grounding a humanoid theory of language

A model for hypermedia learning environments based on electronic books

Designers of hypermedia learning environments could take advantage of a theoretical scheme which takes into account various kinds of learning activities and solves some of the problems associated with them. In this paper, we present a model which inherits a number of characteristics from hypermedia and electronic books. It can provide designers with the tools for creating hypermedia learning systems, by allowing the elements and functions involved in the definition of a specific application to be formally represented A practical example, CESAR, a hypermedia learning environment for hearing‐impaired children, is presented, and some conclusions derived from the use of the model are also shown

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

The Matter of Entrepreneurial Learning: A Literature Review

This paper is a comprehensive review of the entrepreneurial learning literature and its engagement with the material aspects of entrepreneurship, as part of the “material turn” in the social sciences. Drawing on actor-network theory, we construct a classificatory scheme and an evaluative matrix to find that this field is dominated by an anthropocentric bias and cognitivist approaches which largely ignore issues of materiality in entrepreneurship. However we also identify some heterogeneous network-based conceptualisations of entrepreneurial learning which could provide the foundations for more materially aware approaches. We conclude by calling for a material turn in entrepreneurial learning and outline some possible avenues for it

OntoAna: Domain Ontology for Human Anatomy

Today, we can find many search engines which provide us with information which is more operational in nature. None of the search engines provide domain specific information. This becomes very troublesome to a novice user who wishes to have information in a particular domain. In this paper, we have developed an ontology which can be used by a domain specific search engine. We have developed an ontology on human anatomy, which captures information regarding cardiovascular system, digestive system, skeleton and nervous system. This information can be used by people working in medical and health care domain.Comment: Proceedings of 5th CSI National Conference on Education and Research. Organized by Lingayay University, Faridabad. Sponsored by Computer Society of India and IEEE Delhi Chapter. Proceedings published by Lingayay University Pres