James and project collaborator Naomi Leonard (Princeton) co-organised a minisymposium on ‘Excitability, Feedback and Collective Decision-Making Dynamics’, at the 2017 SIAM Meeting on Dynamical Systems, in Snowbird.
Thomas contributed one of four talks on decision-making dynamics, exploring the roles of excitability and feedback in neural and collective decision systems. The speakers were:
Excitability and Feedback in Regulation of Foraging Harvester Ants – Renato Pagliara, Princeton University, USA; Deborah M. Gordon, Stanford University, USA; Naomi E. Leonard, Princeton University, USA
Models of Value-Sensitive Decision Making – Thomas Bose, Andreagiovanni Reina, and James A R Marshall, University of Sheffield, United Kingdom
Coupled Drift-diffusion Model and the Speed-accuracy Tradeoff in Collective Decision-making – Vaibhav Srivastava, Michigan State University, USA
Evidence Accumulation in Dynamic Environments – Zachary P. Kilpatrick, University of Colorado Boulder, USA; Adrian Radillo and Kresimir Josic, University of Houston, USA; Alan Veliz-Cuba, University of Dayton, USA
The video above showcases the functionalities of ARK through three demos. In Demo A, ARK automatically assigns unique IDs to a swarm of 100 Kilobots. Demos B shows the possibility of employing ARK for the automatic positioning of 50 Kilobots, which is one of the typical preliminary operations in swarm robotics experiments. These operations are typically tedious and time consuming when done manually. ARK saves researchers’ time and makes operating large swarms considerably easier. Additionally, automating the operation gives more accurate control of the robots’ start positions and removes undesired biases in comparative experiments. Demo C shows a simple foraging scenario where 50 Kilobots collect material from a source location and deposit it at a destination. The robots are programmed to pick up one virtual flower inside the source area (green flower field), carry it to the destination (yellow nest), and deposit the flower there. When performing actions in the virtual environments, the robot signals by lighting its LED in blue. When picking up a virtual flower from the source, the robot reduces the source’s size for the rest of the robots (by reducing the area’s diameter by 1cm). Similarly when a robot deposits flowers at its destination, the area increases by 1 cm. This demo shows that robots can perceive (and navigate) a virtual gradient, can modify the virtual environment by moving material from one location to another, and can autonomously decide when to change the virtual environment that they sense (either the source or the destination).
More information available at: http://diode.group.shef.ac.uk/kilobots/index.php/ARK
The DiODe team organises a minisymposium at the Mathematical Models in Ecology and Evolution conference taking place in London this July. In this minisymposium, recent progress on collective behaviour and decision making will be discussed by a selection of excellent speakers.
Two new papers with results of the DiODe project have been accepted recently. The review article entitled Collective Decision Making, which appeared in the journal Current Opinion in Behavioural Sciences, summarises recent progress in natural and artificial collective decision making. The other paper entitled A model of the best-of-N nest-site selection process in honeybees has been accepted for publication in Physical Review E and generalises in a theoretical study the nest site selection of honeybees to three and more options.