 The following are abstracts from representative LIS Research Awards for FY 1997. These examples are provided for illustrative purposes only, and are not intended to be restrictive.
Understanding and Fostering Spatial Competence
A group of nine senior investigators will study spatial competence, and its emergence over time, at the cognitive, computational, and neural levels. Topics to be studied include how people form spatial representations; how people communicate about spatial information using external symbol systems such as maps, diagrams, graphs, and linguistic descriptions; the role of the educational input received in American schools in supporting spatial learning; the optimal computational model of spatial learning; and, evidence of neural plasticity for spatial learning, based on both neuroanatomical study and neuropsychological evaluation. The common purpose of this group of related research endeavors is to examine the nature of environmentally-sensitive growth in spatial competence and how spatial learning can be maximized in the American population. Innovations for educational practice and educational software resulting from our research will be evaluated with the help of collaborating teachers. 
Simulating Tutors with Natural Dialog and Pedagogical Strategies
The long-term practical objective of the research is to develop a fully automated computer tutor. The tutor would be able to
The tutor's discourse moves include: pumping, prompting, hinting, questioning, answering, summarizing, splicing in correct information, providing immediate feedback, and rewording student contributions. The dialog contributions of the tutor would be in different formats and media: printed text, synthesized speech, simulated facial movements, graphic displays, and animation. Such an achievement will require an interdisciplinary integration of theory and empirical research from the fields of cognitive psychology, discourse processing, computational linguistics, artificial intelligence, human-computer interaction, and education. The tutoring topics will be in the domains of computer literacy and introductory medicine. Previous attempts to develop a fully automated tutor have been seriously challenged by some technical and theoretical barriers. These include:
Recent advances have dramatically reduced these barriers, so it is time to revisit the mission of developing an automated tutor. According to the recent research on human tutoring, a key feature of effective tutoring lies in generating discourse contributions that assist learners in actively constructing explanations, elaborations, and mental models of the material. The proposed research will advance scientific understanding of how a tutor can manage a smooth, polite dialog that promotes deep learning of the material.
An Integrated Approach to Concept Learning in Humans and Machines
Concepts are essential for intelligent thought and action. The goal of the project is an integrated view of concept learning in humans and machines. The primary focus will be combining psychological experimentation with artificial intelligence modeling to examine the interaction of world knowledge and empirical information during concept learning. The representation of concepts consists of feature regularities observed in the instances and of features inferred from world knowledge. However, current theories focus on only one type of feature and do not consider how learning each might affect the other. Additional work will examine how the use of concepts (such as those used for problem solving) may affect learning, how prior knowledge may be restructured to accommodate new information, and how concepts may change with age and experience. Computational learning theory will be adapted to provide a mathematical characterization of the learning process. The view of concept learning that results from this work will be integrated in that it will:
The first goal will place greater constraints on theoretical accounts, suggest new possibilities, and help to decide among competing explanations. The second goal will lead to a theory that is psychologically and computationally plausible, yet sufficiently rigorous to be analyzed with the mathematical tools of computational learning theory. Such a theory will contribute to the generation of new knowledge by broadening the understanding of concept learning in each of the fields, and by promoting new research issues and approaches in each field through interdisciplinary work.
Structured Statistical Learning
Learning in many cognitive domains, including language and vision, involves recognition of complex hierarchical structure that is hidden or only indirectly reflected in the input data. In this project a multi-disciplinary group of applied mathematicians, cognitive scientists, computer scientists, linguists, and neuroscientists will study the learning of compositional structure in language, vision, and planning, and will also probe the neural mechanisms for identifying and exploiting such structure. The research involves three interacting lines of work. The first refines and extends statistical learning models; the second applies these models to language, vision, and planning; and the third develops and applies new experimental and analysis techniques for probing the neural mechanisms that learn and exploit compositional structure. The results of the project should significantly increase our understanding of complex learning, and should have implications for a wide range of topics in education (e.g., learning of complex knowledge structures in science and math) and technology (e.g., automated speech recognition, computer vision, robotics). This project is being funded through the Learning & Intelligent Systems Initiative, and is supported in part by the NSF Office of Multidisciplinary Activities in the Directorate for Mathematical & Physical Sciences.
Learning Minimal Representations for Visual Navigation and Recognition
This project is concerned with the intelligence exhibited in interactions among sensory-motor activities and cognitive capacities such as reasoning, planning and learning, in both organisms and machines. Such interaction is regularly shown in the act of navigation, which is engaged in by humans and other animals from an early age, and seems almost effortless in normal circumstances thereafter. Whatever there is in navigation that is innate and whatever is learned, it is important to try to understand the interaction of the various cognitive, perceptual, and motor systems that are involved. The complexity of these interactions becomes clear in the development of mobile robots, such as the one recently deployed on Mars, not to mention the more autonomous ones planned for the future. It is still a major and imperfectly understood task to create programs that will coordinate sensors, keep an internal "map" of the area, and allow the robot to cross a space efficiently and without collisions with obstacles. An interdisciplinary approach is being taken in this research project, exploring human capabilities through experiments, developing models based on the experimental results and what is already known about human navigation, implementing these models in programs for robot control, then testing these programs in robotic navigation experiments for their efficacy and their reasonableness as models of human navigation. The goals are both to understand the phenomena in humans and machines and to develop robust algorithms to be used in mobile robots. This alliance of researchers studying psychophysics, cognition, computation, and robotics will lead to gains in knowledge across many disciplines and will enhance our understanding of spatial cognition and visual navigation in agents, both artificial and natural.
Optimization in Language and Language Learning
This project is interdisciplinary research in the knowledge, processing, and learning of language. It proceeds from a framework utilizing results from mathematical statistics, adaptive systems, and formal learning theory which provide a means of treating language as a kind of statistical optimization. Previous work by the principal investigator on the integration of linguistic theory with optimization principles in neural networks has led to this new grammar formalism, optimality theory, which has had considerable impact on many aspects of the study of human language, including learning. Recently developed methods of psychological experimentation now provide reliable data on the process of language learning, even in infants. This research brings together these experimental methods for observing real-time processing and learning of language, computational methods of research on optimization and automatic language processing, and linguistic methods for studying the structure of the representations essential for human language. The investigators bring not only expertise in the contributing disciplines, but also considerable experience in interdisciplinary collaboration. The results of this research may help to explain the mystery of how humans - and possibly artificial systems - can learn to use and understand languages.
Developmental Motor Control in Real and Artificial Systems
A key aim of this initiative is to understand how highly complex intelligent systems could arise from simple initial knowledge through interactions with the environment. The best real-world example of such a system is the human infant who progresses from relatively simple abilities at birth to quite sophisticated abilities by two-years-of-age. This research focuses on the development of reaching by infants because:
The project brings together two computer scientists who are experts on learning control algorithms and neural networks, and two psychologists who are experts on the behavioral and neural aspects of infant reaching, to investigate and test various algorithms by which infants might gain control over their arms. The proposed research focuses on the control strategies that infants use in executing reaches, how infants develop appropriate and adaptive modes of reaching, the mechanisms by which infants improve their ability to reach with age, the role of sensory information in controlling the reach, and how such knowledge might be stored in psychologically appropriate and computationally powerful ways. Preliminary results suggest that computational models that are appropriate for modeling the development of human reaching are different in significant ways from traditional computational models. Understanding the mechanisms by which intelligence can develop through learning can have significant impact in many scientific and engineering domains because the ability to build such systems would be simpler and faster than engineering a system with the intelligence specified by the engineer and because systems based on interactive learning could rapidly adapt to changing environmental conditions.
Knowledge-based Action Planning and Control Problems in Engineering and Biology
Biological systems have an innate capability for learning and representation of dynamical cues from the environment and a navigational and gaze control capability to sustain themselves in an unstructured environment. Such a paradigm is largely lacking in engineered and artificial navigational systems. Man-made systems, (viz. a walking or a mobile robot), are designed and manufactured on the basis of a specific task objective with little emphasis on the design of a feedback control system. The proposed research interests include microcircuits of motion detection in the visual cortex of a turtle; pattern recognition and visual attention in primate visual system; muscle dynamics, head eye coordination and asymptotic tracking; information feedback and learning. We would develop a suitable algorithm that would learn from its visual cues and visually predict the motion of a target in a cluttered environment; we would determine what are the biological mechanisms that allow us to attend to a selected region of a visual space; we would develop an algorithm to coordinate the motion of head and eye for the purpose of gaze control and asymptotic- tracking; and we hope understand "dynamical problems in perception," introducing dynamical systems with perspective observation geometry. The goal is to derive algorithms that would visually estimate the motion parameters in a dynamically changing scene using biologically inspired models of the retina and Information-coding. An intelligent system needs to control the flow of information through judicious choice of its scarce resources. This team proposes to introduce and investigate a new Information Guided Feedback Paradigm for improved perception, learning, action planning and control that is an important research problem with a tremendous potential for education. Engineers would learn from biological systems as to how machines (robots) of the future could integrate sensory (visual) knowledge, build an internal representation (based on neural coding) and be guided by information feedback towards an improved man/machine interaction.
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