Workshop: Swarm Intelligence in AI and ALife
The 3rd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM2019)will bring together a diverse community interested in the engineering of living things, frombiomechanics to swarm intelligence, and the perpetuation of research at the intersection ofbiology and engineering.
Swarm intelligence is not just the result of self-organization. Component agents differentiate,
communicate with each other, and create super-organisms. Using various model simulations andsocial animals and insects as examples, we will discuss the swarm intelligence of natural andartificial systems and their complexity. In this workshop, we invite presentations by Prof. Marco
Dorigo and other interesting researchers in the fields of AI and ALife.
Location:
Room B (Meeting Room #1), Conference Center, OIST [ Google
Maps : https://goo.gl/maps/SsJ8RBKsYz7uaPxC7 ]Workshop Leaders:
Takashi Ikegami (University of Tokyo), Hemma Philamore (Kyoto University)

Workshop program :13:00

Introduction

13:15

Matthew Turner, University of Warwick
Intrinsically motivated collective motion
We study a simple model of information-processing (living) agents. These agentsseek maximal control of their environment via "future state maximisation" (FSM), a
principle that may connects fitness with information processing more generally. We
study moving, re-orientable agents. The action of each agent is (re)established byexhaustive enumeration of its future decision tree at each time step - each agent
chooses the branch of its tree leading from the present to the richest future state
space. Cohesive swarm-like motion emerges that is similar to that observed inanimal systems, such as bird flocks. We develop heuristics that mimiccomputationally intensive FSM but that could operate in real time under animalcognition. This offers a philosophically attractive, bottom-up mechanism for theemergence of swarming.

13:45

Olaf Witkowski, Cross Labs
Open-ended swarms: the layered evolution of collective intelligence
Since life originated on Earth, it seems to have indefinitely been increasing in levelsof intelligence and complexity. Simple organisms had to self-organize into dynamical

networks to gather, internalize, compress and reorganize relevant information about
their own persistence, from perpetually changing environments. The learning
process undergone by all life can be understood as a long series of self-maintaining
information flows through time and space, which enabled organisms to makeincreasingly better predictions, eventually reaching much faster learning timescales
than Darwinian evolution, and achieving impressively generalized knowledge about
the universe. In this talk, I will present an approach to the study of this open-ended
evolution via the simulation of collective dynamics in autonomous agents, giving riseto layer after layer of dynamics. The model highlights three important factors in thecreation of novelty and diversity: (a) communication generates combinatorial
cooperative dynamics, (b) concurrency allows for separation of time scales, and (c)complexity and size increases push the system towards transitions in innovation.
These three components are illustrated in a model computing the continuous
evolution of a swarm of agents, thus contributing to our understanding of thecharacteristics and layered structure of collective intelligence.
14:15 BREAK
14:30

Giovanni Reina, University of Sheffield
Collective decision making: search, vote, and agree
I will give an overview of my studies on collective decision making in distributed
systems of the last eight years. Such systems, found in biology, sociology, andengineering, are composed of a large number of interacting individuals thatcoordinate in order to reach a consensus. The main phases of the collective decision
making process consist of identifying the available options, estimating their quality,
and selecting the best option or any of them. I will present the main results of myresearch in understanding and designing each of these phases. Collective systems
are inherently difficult to analyse as the stochastic nonlinear interactions between
individuals can give rise to complex emergent dynamics. Therefore, I employ acollection of advanced techniques, commonly defined as multiscale modelling.
Relying on a set of methods, rather than a single one, gives the benefit of having
complementary techniques addressing one another's limitations. In fact, throughmultiscale modelling, it is possible to analyse the systems at various levels ofcomplexity and detail, from macroscopic group-level dynamics to microscopic
individual-level behaviour, and from noise-free deterministic models to stochastic
spatial descriptions. I finally shed a light on the recently developed opensource
software for automated multiscale modelling.

15:00

Hiroyuki Iizuka, Hokkaido University
AI applications to swarm behaviors
In this talk, I will introduce AI application studies toward swarm behaviors. One is amachine learning method to obtain the sensorimotor mapping of individuals directlyfrom data captured in a real fish swarm. The individual positions are detected andthe sensory inputs and motor outputs are estimated and used as training data. Asimple feedforward neural network is trained to learn the sensorimotor mapping ofindividuals. As a result, our trained neural network could reproduce the swarm

behavior better than the Boids model. Another example is to establish a real-time
interaction between real and virtual fish. The computer display is attached on thetank and thefish can see the virtual fish. The real fish movements are captured by a camera andthe virtual fish can decide the movements in response to the real fish. The behaviorsof the real fish are evaluated and compared with the condition with virtual playback
fish. I will discuss about the further possibilities to apply AI to swarm.
15:30

Marco Dorigo, Université Libre de Bruxelles
Collective Decision Making: The Best-of-n Problem in Robot Swarms
Achieving fast and accurate collective decisions with a large number of simple
agents without relying on a central planning unit or on global communication isessential for developing complex collective behaviours.
In this talk, I will present recent research done in my lab on collective decision
making in robot swarms. In particular, I will present opinion-based methods for thesolution of a few variants of the best-of-n problem using a swarm of real robots.

16:15

Closing remarks

16:30

END