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Robots are increasingly capable of co-existing with human beings in the places where we live and work. I believe, however, for robots to collaborate and assist human beings in their daily lives, new methods are required for enhancing humanrobot communication. In this dissertation, I focus on how a robot can acquire and refine expressive and receptive communication skills with human beings. I hypothesize that communication has its roots in motor behavior and present an approach that is unique in the following aspects: (1) representations of humans and the skills for interacting with them are learned in the same way as the robot learns to interact with other “objects,” (2) expressive behavior naturally emerges as the result of the robot discovering new utility in existing manual behavior in a social context, and (3) symmetry in communicative behavior can be exploited to bootstrap the learning of receptive behavior. Experiments have been designed to evaluate the approach: (1) as a computational framework for learning increasingly comprehensive models and behavior for communicating with human beings and, (2) from a human-robot interaction perspective that can adapt to a variety of human behavior. Results from these studies illustrate that the robot successfully acquired a variety of expressive pointing gestures using multiple limbs and eye gaze, and the perceptual skills with which to recognize and respond to similar gestures from humans. Due to variations in human reactions over the training subjects, the robot developed a preference for certain gestures over others. These results support the experimental hypotheses and offer insights for extensions of the computation framework and experimental designs for future studies.

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Richard Skemp's theory of conceptual structures (schemas) was adapted by hypothesizing two types of schemast (a) an underlying parent schema; and (b) a problem solving schema which contains paths for problem solving. Three interpretations of the definitions of the trigonometric functions were identified: (a) the right triangle interpretation; (b) the coordinate system interpretation; and (c) the unit circle interpretation. For each interpretation representative problems were chosen and methods of solution analysed. From the methods of solutions the relevant concepts and specific actions employed were identified. The parent schema was constructed by placing the concepts in a geometric configuration with two concepts joined by a line if and only if some action joined those concepts. The problem solving schema was formed by joining the concepts at the vertices by lines which were labeled to describe the actions they represent. The preliminary model thus constructed was tested and refined by interviewing four subjects of varying abilities from a trigonometry class and then analyzing those interviews according to instructions developed from the adaptation of Skemp's theory and the problem solving methods. Interview analysis included constructing representations of the parent and problem solving schemas possessed by each subject in each interpretation. The refined Model was then used to interview two additional subjects from another trigonometry class and to analyse the interviews. Interview analysis indicated strong face validity in that the Model was found to contain the problem solving paths used by the subjects. Cross validation was also found to be strong. Reliability of analysis was found to be strong in the less complex schemas and somewhat weaker when more complex schemas were involved. The Model was found to be a viable model of conceptual structures in which problem solving can be described and to have potential value for both instruction and research. Suggestions for further research on the Model and for using the Model in both instruction and research are included. / Ed. D.

published_or_final_version / Education / Master / Master of Education

Experience forms the basis of learning. It is crucial in the development of human intelligence, and more broadly allows an agent to discover and learn about the world around it. Although experience is fundamental to learning, it is costly and time-consuming to obtain. In order to speed this process up, humans in particular have developed communication abilities so that ideas and knowledge can be shared without requiring first-hand experience. Consider the same need for knowledge sharing among robots. Based on the recent growth of the field, it is reasonable to assume that in the near future there will be a collection of robots learning to perform tasks and gaining their own experiences in the world. In order to speed this learning up, it would be beneficial for the various robots to share their knowledge with each other. In most cases, however, the communication of knowledge among humans relies on the existence of similar sensory and motor capabilities. Robots, on the other hand, widely vary in perceptual and motor apparatus, ranging from simple light sensors to sophisticated laser and vision sensing. This dissertation defines the problem of how heterogeneous robots with widely different capabilities can share experiences gained in the world in order to speed up learning. The work focus specifically on differences in sensing and perception, which can be used both for perceptual categorization tasks as well as determining actions based on environmental features. Motivating the problem, experiments first demonstrate that heterogeneity does indeed pose a problem during the transfer of object models from one robot to another. This is true even when using state of the art object recognition algorithms that use SIFT features, designed to be unique and reproducible. It is then shown that the abstraction of raw sensory data into intermediate categories for multiple object features (such as color, texture, shape, etc.), represented as Gaussian Mixture Models, can alleviate some of these issues and facilitate effective knowledge transfer. Object representation, heterogeneity, and knowledge transfer is framed within Gärdenfors' conceptual spaces, or geometric spaces that utilize similarity measures as the basis of categorization. This representation is used to model object properties (e.g. color or texture) and concepts (object categories and specific objects). A framework is then proposed to allow heterogeneous robots to build models of their differences with respect to the intermediate representation using joint interaction in the environment. Confusion matrices are used to map property pairs between two heterogeneous robots, and an information-theoretic metric is proposed to model information loss when going from one robot's representation to another. We demonstrate that these metrics allow for cognizant failure, where the robots can ascertain if concepts can or cannot be shared, given their respective capabilities. After this period of joint interaction, the learned models are used to facilitate communication and knowledge transfer in a manner that is sensitive to the robots' differences. It is shown that heterogeneous robots are able to learn accurate models of their similarities and difference, and to use these models to transfer learned concepts from one robot to another in order to bootstrap the learning of the receiving robot. In addition, several types of communication tasks are used in the experiments. For example, how can a robot communicate a distinguishing property of an object to help another robot differentiate it from its surroundings? Throughout the dissertation, the claims will be validated through both simulation and real-robot experiments.

published_or_final_version / abstract / toc / Education / Master / Master of Education

M.Ed. / The purpose of this research was to examine misconceptions held by pupils about Ecology and make suggestions on how these misconceptions could be eliminated. Although there has been much published research identifying misconceptions in the understanding of biological concepts by pupils, relatively little has appeared locally to indicate how misconceptions in this field might best be eliminated, changed or minimized. As part of this research, Biology pupils were given a test to complete. The main aim of the test was to identify misconceptions that the pupils held after classroom instruction. The pupils were also given a questionnaire to complete. The questionnaire was designed to assess pupils' attitude towards Biology in general, Ecology and fieldwork in particular. The pupils' knowledge of Ecology following instruction was further assessed by interviews. Teachers were also given questionnaires to complete. The main aim of the teachers' questionnaire was to determine if fieldwork could play any significant role in the teaching of Biology. Furthermore, the teachers' questionnaires also explored some of the problems or limiting factors experienced by teachers when they undertake fieldwork. This study revealed that pupils held misconceptions about energy flow in the ecosystem, basic ecological terms/concepts, nutrient cycling, the food web, etc. The research also suggested that fieldwork helps pupils to understand certain other sections of the Biology syllabus better. It is recommended, amongst others, that experienced Biology teachers, authors of Biology textbooks, lecturers at in-service training centres, and subject advisors should do all they possibly can to assist their colleagues and pupils to avoid misconceptions about Ecology.