NICSO 2007  
Acireale, Sicily (Italy), November 8-10, 2007
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Plenary Speakers

  Paolo Arena - University of Catania

From Emergence to Cooperation: the role of Nonlinear Dynamics in Cognitive Robotics

ABSTRACT: Living creatures show distinct abilities to interact adaptively with their environment. These characteristics find their roots in the self-organizing dynamics of neural circuits, which in nonlinear science represent the highest example of emergent behavior. The lecture will explore the paradigm of biological inspiration for modelling and implementation of adaptive locomotion patterns in biological inspired walking machines.
Once assessed the potentiality of lattices of nonlinear artificial neurons to lead to the emergence adaptive locomotion controllers, endowed with graceful degradation purposes, the fascinating world of perception will be faced with. In fact, it is natural, once developing legged robots able to suitably move, to try to face with the problem of autonomous action planning and environment intelligent interaction.
Even if the term perception is being used more and more frequently in this period, rarely it is referred to considering nonlinear dynamical circuits and systems. In fact it is mostly related to psychological theories, neurophysiological experiments or computer programs. However, it is clear that perceptive information in living systems uses, as substrate, massively connected cells, mutually and massively interacting. In our framework the core of perception is conceived as an emergent, pattern forming, phenomenon.
In our spatial-temporal approach, perception is considered as the result of a dynamic pattern forming process, in which a particular pattern will evolve in a spatial-temporal structure, starting from the information deriving from sensors. This pattern will indeed represent in a concise fashion the environment information. Recent results in neurobiology have shown that this is based on internal representations that combine aspects of sensory input and motor output in an unified way.
This is the essence from which percepts are able to be produced in real time for guiding actions in complex environment.

  Roberto Battiti - University of Trento

Reactive Search: Adaptive on-line Self-Tuning for Optimization

ABSTRACT: Most state-of-the-art heuristics are characterized by a certain number of choices and free parameters, whose appropriate setting is a subject that raises issues of research methodology. In some cases the role of the user as an intelligent (learning) part makes the reproducibility of heuristic results difficult and, as a consequence, the competitiveness of alternative techniques depends in a crucial way on the user's capabilities. Reactive Search advocates the use of simple sub-symbolic machine learning to automate the parameter tuning process and make it an integral (and fully documented) part of the algorithm. The word "reactive" hints at a ready response to events during the search through an internal online feedback loop for the self-tuning of critical parameters. Task-dependent and local properties of the configuration space can be used by the algorithm to determine the appropriate balance between diversification and intensification. Some interesting novel research directions combining cooperative strategies and Reactive Search will be highlighted.

  Marco Dorigo - Université Libre de Bruxelles

Swarm-bots: An Experiment in Swarm Robotics

ABSTRACT: Swarm robotics is the study of how collectively intelligent behaviors can emerge from local interactions of a large number of relatively simple physically embodied agents. In this talk I will discuss results of the Swarm-bots experiment in swarm robotics. A swarm-bot is an artifact composed of a swarm of assembled s-bots. The s-bots are mobile robots capable of connecting to, and disconnecting from, other s-bots. In the swarm-bot form, the s-bots are attached to each other and, when needed, become a single robotic system that can move and change its shape. S-bots have relatively simple sensors and motors and limited computational capabilities. A swarm-bot can solve problems that cannot be solved by s-bots alone. In the talk, I will shortly describe the s-bots hardware and the methodology we followed to develop algorithms for their control. Then I will focus on the capabilities of the swarm-bot robotic system by showing video recordings of some of the many experiments we performed to study coordinated movement, path formation, self-assembly, collective transport, shape formation, and other collective behaviors.