Workshop Material
From SCWiki
Contributions from Keynote Speakers
Prof. Kevin Passino: Swarm Cognition
Findings from neuroscience, psychology, and behavioral biology have been synthesized to show that some key features of cognition in the neuron‐based brains of vertebrates are also present in the insect‐based swarm of honey bees. The key ideas have been presented in the context of the cognitive task of nest‐site selection by honey bee swarms. After reviewing the mechanisms of distributed evidence gathering and processing that are the basis of decision‐making in bee swarms, numerous similarities in the functional organization of vertebrate brains and honey bee swarms were highlighted. Read more
Dr. James Marshall: Colony-level Cogntion
Quick Giude from Current Biology 19(10) pp. R395-R396, 2009
Papers accepted for presentation during the Workshop
James J. Anderson, Bertrand H. Lemasson and R. Andrew Goodwin: Advantages of a retinal-based model for studying swarm cognition
Abstract: We describe the advantages of a retinal-based model of animal swarming over metric-basic models and consider how the retinal model might be useful in addressing several research questions in swarm cognition.
Dan Sayers: Evolved Flocking Under Predation in Simple Vehicles
Abstract: Increasing evidence points to predation as a likely source of evolutionary pressure behind flocking behaviours in animals. In simulation, flocking is shown to be a highly stable strategy for simple agents with steering behaviours, under (co-) evolutionary pressure of predation. A number of perturbations are undergone to assess the stability of the behaviour.
Pedro Santana and Luís Correia: Swarm-Based Active Vision
Abstract: This paper proposes a computational distributed model for active vision. In the proposed model, action selection and visual processes progressively unfold in a parallel and asynchronous way through a set of cross-modulatory signals. The visual process is modelled with a swarm of perceptual agents inhabiting the physical agent’s sensorimotor space, motivated by the ant foraging metaphor. Perceptual agents, called perceptual-ants (p-ants), perform local active vision, whereas the self-organised collective behaviour maintains global spatio-temporal coherence, i.e. a social cognitive map. A by-product of the method is the ability to maintain distributed, active and sparse spatial working memories, i.e. local maps of the environment. Experimental results with a simulated robot, performing a simple navigation task, show the ability of the model to perform both robustly and parsimoniously in terms of processing.
