AISB'03 Keynote speakers - abstracts and biographies

AISB'03 Keynote speakers - abstracts and biographies

Sorin Solomon: "Perception, Cognition and Creativity Emerging from Simple Microscopic Agents"

Talk abstract
The Microscopic Representation method strives to explain complex macroscopic phenomena in terms of the emergent properties of collective objects composed of many simple interacting agents.

This method has been applied in a wide range of systems, first in physical sciences but increasingly in biological, cognitive and social systems.

I will discuss the conceptual basis of the Microscopic Representation paradigm as well as a few examples in visual perception, cognitive development, cognitive immunology, artificial creativity, novelty propagation etc.

R. Rao and A. N. Meltzoff: "Imitation learning in infants and robots: Towards probabilistic computational models"

Talk abstract
Learning through imitation is a powerful and versatile method for acquiring new behaviors. In humans, a wide range of behaviors, from styles of social interaction to tool-use, are passed from one generation to another through imitative learning. Although imitation evolved through Darwinian means, it achieves Lamarckian ends: it is a mechanism for the `inheritance' of acquired characteristics. Unlike conventional trial-and-error-based learning methods such reinforcement learning, imitation leads to rapid learning. This potential for rapid behavior acquisition through demonstration has made imitation learning an increasingly attractive alternative to programming robots.

In this talk, we review recent results on how infants learn through imitation. These results suggest a four stage progression of imitative abilities: (i) body babbling, (ii) imitation of body movements, (iii) imitation of actions on objects, and (iv) imitation based on inferring intentions of others. We formalize these four stages within a probabilistic framework for learning and inference. The framework acknowledges the role of internal models in sensorimotor control and is inspired by recent ideas from machine learning on Bayesian inference in graphical models. We discuss two main advantages of the probabilistic approach: (a) the development of new algorithms for robotic imitation learning in noisy and uncertain environments, and (b) the potential for using Bayesian methodologies (such as manipulation of prior probabilities) and robotic technologies to obtain a deeper understanding of imitation learning in human infants.

Speaker biographies for A. N. Meltzoff and R. Rao
Dr. Andrew N. Meltzoff is a Professor of Psychology at the University of Washington and Co-Director of the UW Center for Mind, Brain and Learning. A graduate of Harvard University, with a PhD from Oxford University, he is an internationally recognized expert on infant and child development. Prof Raj Rao is a prize-winning scientist in the Computer Science and Engineering Department at the University of Washington.

Giulio Sandini: "Human Babies and Robot Cubs"

Talk abstract
(G. Sandini, G. Metta, L. Natale, S. Rao, R. Manzotti)

The role of technology for the study of brain functions has always been fundamental in providing new tools for the acquisition/analysis of biological data. However the increasingly complex picture of brain functions emerging from neuroscience research is now posing a new challenge: how to extend our knowledge beyond the scope of specific experiments and methodologies? Is it possible to find new tools enabling neuroscientists to verify new theories and to guide new experiments beyond the, now established, methods of mathematical modeling and system's theory? The scientific goal of the LIRA-Lab is to investigate if the implementation of artificial systems through physical models is a useful tool to help understanding complex brain functions. The reasons why we believe that this is indeed the case are, essentially, two. The first stems from the very high complexity and non-homogeneity of our current knowledge of brain functions. The second is that the physical world (in a general sense) is far too complicated to be "simulated" realistically preventing adequate testing of new theories and ideas to be performed.

In the context of human cognition, implementing "artificial systems" as explicit physical models of biological ones means implementing humanoid systems. Not with the aim of building an "artificial human being" or more efficient robots but to test assumptions and hypothesis more explicitly. In particular the use of humanoids as tools to understand human cognition, is focused, in our lab, on trying to explain how adaptation develops through interaction with the external environment. Our reference framework is human sensorimotor and cognitive development and we approach the problems by trying to implement motor and cognitive abilities in an artificial system. Is it possible to "program" a system to "have cognition" as we program a robot to assemble a car? Is cognition similar to motor control and sensorimotor coordination? Do we know enough about our own cognitive abilities to transfer them into an artificial being? How do we interact safely and "intelligently" with other humans (and machines)? How do we predict the effects of our actions? How do we adapt our behavior to unpredictable situations? How do we anticipate what other humans are doing? Can all (or even some) of these abilities be hard coded into a humanoid robot? Looking at natural systems it seems that pre-coding cognitive and adaptive behaviors is not possible (we believe that adaptive behaviors cannot be pre-programmed).

In this talk I will claim that if future robots have to have cognitive abilities, they will have to go through developmental phases similar to those found in human babies. I will do that from a multidisciplinary perspective by presenting findings derived from studies of human motor and cognitive development as well as a robotic implementation of the first few months of "existence" of a robot cub (Babybot). In doing so I will stress the consequences that this multidisciplinary approach has in discovering new technologies and the relevance that robotics research will continue to have as a research tool to understand human cognition.

1.Sandini, G., G. Metta, and J. Konczak.Human Sensori-motor Development and Artificial Systems. in International Symposium on Artificial Intelligence, Robotics and Intellectual Human Activity Support(AIR&IHAS '97). 1997. RIKEN - Japan.

2.Metta, G., G. Sandini, and J. Konczak, A Developmental Approach to Visually-Guided Reaching in Artificial Systems. Neural Networks, 1999. 12(10): p. 1413-1427.

3.Natale, L., S. Rao, and G. Sandini. Learning To Act On Objects. in 2nd Workshop on Biologically Motivated Computer Vision. 2002. Tuebingen, Germany.

Acknowledgements: Research decribed here is supported by the EU project COGVIS (IST-2000-29375) and MIRROR (IST-2000-28159) and by the Italian Space Agency.

Speaker biography for Giulio Sandini
The research activity of LIRA-Lab is in the field of Computational Neuroscience and Neuro-IT with the objective of understanding the neural mechanisms of human sensorimotor coordination and cognitive development by realizing anthropomorphic artificial systems such as humanoids (Project Babybot). With our baby humanoid "Babybot" we have contributed to the study of development of eye movements control, visuo-inertial integration, eye-head coordination, visually guided reaching.

Prof. Giulio Sandini is a full professor of the Faculty of Engineering of the University of Genova and founder of the LIRA-Lab (Laboratory for Integrated Advanced Robotics).

The leading theme of his research activity has been visual perception and sensorimotor coordination from a biological and an artificial perspective.

Peter Hobson: "The interpersonal origins of thinking: How humans achieve what computers (so far) haven't"

Talk abstract
There is something remarkable that happens in the course of the first two years of life: infants leave infancy behind, and become participants in human culture. They not only begin to talk and to play symbolically, but they also become able to think about things and events and people, taking up this and then another subjective perspective on the "objects" of thought. These accomplishments prompt us to ask: How on earth do they (and how on earth did we) accomplish such a feat? And what would it take for computers to cross the rubicon into thought?

In this presentation, I shall address just one facet of this intriguing problem. Through the study of typically developing infants and children with autism, we may come to appreciate the significance of one component of the developmental process: the ability for an infant to identify (often through feelings) with the subjective orientation of other people, both in one-on-one interactions and in relation to a shared external world.

I shall present some evidence that bears upon this issue.

Speaker biography for Peter Hobson
Peter Hobson is Tavistock Professor of Developmental Psychopathology in the University of London, at the Tavistock Clinic and the Department of Psychiatry and Behavioural Sciences, UCL.

Prof Hobson is a psychiatrist, has a PhD in experimental psychology from the University of Cambridge, and is a psychoanalyst. His principal research interests are autism, early child development and adult personality disorder. His recent book, The Cradle of Thought (MacMillan, 2002), attempts to integrate these perspectives in a developmental account of the development of symbolic thinking.

Yiannis Aloimonos: "Visual space-time geometry: a geometry of thought"

Talk abstract
This talk advances a viewpoint that in general may be considered as a perceptual theory of the mind. The basic thesis is that the content of the mind is organized in the form of a model of the external world which contains objects, events (actions) and their relationships. That means that thinking is essentially a process of manipulating perceptual models of the external world. Roughly speaking, a representation of an event, an act or an object in our heads is a set of moving pictures, that is, video. It is not a conventional video but one that can be seen from any viewpoint, a sort of 3D video. I will describe, in simple terms, a number of basic results that we obtained over the past several years that make it possible to acquire descriptions of the world using video cameras and computer power. Such descriptions have many applications to today's technology, such as virtual and augmented reality, teleimmersion, telepresence and the like. In general, they constitute tools for both perception and imagination. The availability of such models however makes it possible, for the first time in the history of human knowledge, to work towards a computational theory of the mind that is inherently perceptual. The second part of the talk will provide steps towards such a theory and will concentrate on the language problem. I will argue that a universal grammar is inherently related to the geometric and statistical operators responsible for segmentation in images. I will conclude with the outline of a research program on the geometry of the mind that uses action as a quantum.

Speaker biography for Yiannis Aloimonos
Yiannis Aloimonos studied Mathematics in Athens Greece (Dipl. 1982) and Computer Science at the University of Rochester, NY (PhD. 1987). He is currently the Director of the Computer Vision Laboratory at the Univ. of Maryland and a Professor of Computational Vision at the Dept. of Computer Science. His major interest is the relationship of action to intelligence. He is known for his work in Active Vision and Motion Analysis. He has authored and coauthored several books including one on Artificial Intelligence, with Tom Dean and James Allen.

The address contains a description of his research including a Socratic dialogue written at the level of a Scientific American Article. His next monograph with C. Fermuller entitled VISUAL SPACE -TIME GEOMETRY: A geometry of the mind, is expected at the end of the year.

Gregor Schöner: "Dynamic field theory and embodied cognition"

Talk abstract
Overt behavior is always based on a direct link between sensory and motor surfaces. Moreover, behavior is typically adjusted to the perceived environment, a current "task" setting, and to longer term goals. These two boundary conditions of behavior are emphasized in the "embodied cognition" approach to cognition.

This talk introduces the concepts of dynamic field theory, in which simple cognitive properties such as decision making and working memory emerge from a mathematical description of behavior that remains close to sensori-motor processes. The mathematical framework is provided by dynamical systems theory. Autonomous robots designed in terms of these concepts will be used to exemplify the concepts. How these ideas can be used to analyze human behavior will be illustrated drawing on examples from motor control and the development of action planning.

Speaker biography for Gregor Schöner
Gregor Schöner is Professor of Neuroinformatics, Chair for Theoretical Biology at Institut für Neuroinformatik, Ruhr-Universität Bochum, in Germany. He has a PhD in theoretical physics from the Universität Stuttgart.

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