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University of Hertfordshire |
| School of Computer Science |
Homepage Daniel Polani
NIPS 2008
Mini-Symposium (Vancouver, 11. Dec. 2008)
Workshop(Whistler, 12. Dec. 2008)
Principled Theoretical Frameworks for the Perception-Action Cycle
Description
A significant emphasis in trying to achieve adaptation and learning in
the perception-action cycle of agents lies in the development of
suitable algorithms. While partly these algorithms result from
mathematical constructions, in modern research much attention is given
to methods that mimic biological processes.
However, mimicking the apparent features of what appears to be a
biologically relevant mechanism makes it difficult to separate the
essentials of adaptation and learning from accidents of
evolution. This is a challenge both for the understanding of
biological systems as well as for the design of artificial ones.
Therefore, recent work is increasingly concentrating on identifying
general principles rather than individual mechanisms for biologically
relevant information processing. One advantage is that a small
selection of principles can give rise to a variety of - effectively
equivalent - mechanisms. The ultimate goal is to attain a more
transparent and unified view on the phenomena in question.
Possible candidates for such principles governing the dynamics of the
perception-action cycle include but are not limited to information
theory, Bayesian models, energy-based concepts or group-theoretical
principles. The workshop aims at bringing together various
principle-based directions for the investigation of various aspects of
the perception-action cycle and at identifying promising directions of
work.
Schedule
| Mini-Symposium | Workshop (Room Change: Glacier Room/Westin) |
| 11. Dec. 2008 | 12. Dec. 2008 | |
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| | 07:30 | Information Theory and the Perception-Action Loop |
| | | Naftali Tishby, Hebrew University |
| | 08:45 | Discussion/Coffee |
| | | |
| | 09:15 | Information Bottleneck Optimization with Spiking Neurons with Application to Predictive Coding |
| | | Lars Büsing, University of Graz |
| | 10:00 | Bayesian Modelling of a Sensorimotor Loop: Application to Handwriting |
| | | Estelle Gilet, CNRS - INRIA Rhône Alpes |
| | 10:30 | Noon Break |
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| 13:30 | Introduction | | |
| Naftali Tishby, Hebrew University and Daniel Polani, University of Hertfordshire | | |
| 13:50 | Encoding the Location of Objects with a Scanning Sensorimotor System | | |
| David Kleinfeld, UC San Diego | | |
| 14:40 | Break | | |
| | | |
| 14:50 | Perception and Action in Robotics | 15:30 | Information Theory and the Perception-Action Loop |
| Sebastian Thrun, Stanford University | | Daniel Polani, University of Hertfordshire |
| 15:40 | Dealing with risk in the perception-action cycle | 16:30 | Coffee |
| Yael Niv, Princeton University | | |
| 16:30 | End | 17:00 | Are power-laws in human behaviour caused by critical adaptive control? |
| | | Felix Patzelt, University of Bremen |
| | 17:30 | Fundamental Dynamic Properties of Coupled Systems |
| | | Stefan Winter, Oliver Wyman Consulting |
| | 18:00 | Empowerment: The External Channel Capacity of a Sensorimotor Loop and a Method for its Estimation |
| | | Tobias Jung, University of Texas at Austin |
| | 18:30 | End of Workshop |
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Minisymposium
Naftali Tishby, Hebrew University and Daniel Polani, University of Hertfordshire
David Kleinfeld, UC San Diego
| Sensory perception in natural environments involves the dual challenge to encode external stimuli and manage the influence of changes in body position that alter the sensory field. We examined the mechanisms used to integrate sensory signals elicited by both external stimuli and motor activity through the use of behavioral, electrophysiological, and computation tools in conjunction with the vibrissa system of rat. We show that the location of objects is encoded in an ""region-of- interest centered"", as opposed to ""body-centered"", coordinate system. The underlying circuit for this computation is consistent with gating by a shunt, a common motif in cortical circuitry. | |
Sebastian Thrun, Stanford University
| This overview talk investigates the perception action cycle from the robotics perspective. The speaker will discuss existing robotic implementation, discuss alternative architectures, and provide insights from the field of robotics. The speaker led the winning DARPA Grand Challenge team and heads Stanford's autonomous car research group. He will provide ample examples from real-time control of self-driving cars in complex traffic situations. | |
Yael Niv, Princeton University
| Risk (or outcome variance) is omnipresent in the natural environment, posing a challenge to animal decision-making as well as to artificial agents. Indeed, much empirical research in psychology, economics and ethology has shown that humans and animals are sensitive to risk in their decision-making, often preferring a small but certain outcome to a probabilistic outcome with a higher expected payoff. In light of this it may be surprising that optimal control methods such as reinforcement learning do not explicitly take risk into account. In this talk, I will review some of the literature on behavioral risk sensitivity in humans and in animals. I will then discuss a number of recent studies in which the neural basis of risk sensitivity has been investigated. While it is not surprising that the brain represents risk, the role of risk in decision making is still far from clear. I will argue that risk might play a more integrated role in learning and action selection than was previously postulated, specifically through the mechanism of reinforcement learning. However, this still leaves open the question of what we should learn from this neural solution: are there principled (normative) reasons for the prevalence of risk sensitivity? Does a general solution to optimal action selection have to take risk into account? | |
Workshop
Naftali Tishby, Hebrew University
Lars Büsing, University of Graz
| We apply the online learning algorithm for IB optimization to the predictive coding task outlined in (Bialek et al., 2007) on the closed loop. | |
Estelle Gilet, CNRS - INRIA Rhône Alpes
| This paper concerns the Bayesian modelling of a sensorimotor loop. We present a preliminary model of handwriting, that provides both production of letters and their recognition. It is structured around an abstract internal representation of letters, which acts as a pivot between motor and sensors models. The representation of letters is independent of the effector usually used to perform the movement. We show how our model allows to solve a variety of tasks, like letter reading, recognizing the writer, and letter writing (with different effectors). We show how the joint modelling of the sensory and motor systems allows to solve reading tasks in the case of noisy inputs by internal simulation of movements. | |
Daniel Polani, University of Hertfordshire
| We outline recent approaches to characterize the sensorimotor loop of agents from an informational perspective. This view allows the principled characterization of agent behaviours in an environment and of possible paths towards minimalistic AI. | |
Felix Patzelt, University of Bremen
| The perception-action cycle can be seen as closed control loop. A classical control paradigm is to stabilize an inverse pendulum. When humans solve this task while balancing a stick or during upright standing, their behaviour exhibits non-Gaussian fluctuations with long-tailed distributions contrasting technical controllers. The origin of these fluctuations is not known, but their statistics suggests a fine tuning of the underlying system to a critical point. We investigated whether this self-tuning may be caused by the annihilation of local information due to success of control. We found that it can lead to critical noise amplification, a fundamental principle, which produces complex dynamics even in very low-dimensional state estimation tasks. It generally emerges when an unstable dynamics becomes stabilized by an adaptive controller that has a finite memory. Starting from this theory, we developed a realistic model of adaptive closed loop control by including constraints on memory and delays. To test this model, we performed psychophysical experiments where humans balanced an unstable target on a screen. It turned out, that the model reproduces the long tails of the distributions together with other characteristics of the human control dynamics. Fine-tuning the model to match the experimental dynamics identifies parameters characterizing a subject's control system which can be independently tested. Our results suggest, that the nervous system involved in closed loop motor control nearly optimally estimates system parameters on-line from very short epochs of past observations. | |
Stefan Winter, Oliver Wyman Consulting
Tobias Jung, University of Texas at Austin
| Empowerment, the external channel capacity of a sensorimotor loop has been introduced recently as a quantity, similar to predictive information to characterize the sensorimotor efficiency and evaluate the compatibility of the niche of an with its sensorimotor loop, and its quality. While computable in simple scenarios, its evaluation in continuous or higher-dimensional situations is still difficult. Here, we present a computational approach to that purpose and demonstrate an instructive application. | |
Last changed at Sun Dec 7 00:23:42 2008 by D. Polani
