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ALIFE WORKSHOPS, TUTORIALS, & SPECIAL SESSIONS

Dates - July 13-18, 2020
Location - Virtual
Organizers - Vermont Complex Systems Center, University of Vermont
Hashtag - #ALIFE2020


  • WORKSHOPS
    ALife for Social and Environmental Good (ALife4Good)

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    Alife Slack Channel: #workshop-ALife4Good

    Keywords: artificial life, social good, sustainability, biosphere preservation, AI for good, positive future impact

    Organizers: Olaf Witkowski (Cross Labs, Tokyo, Japan), Alan Dorin (Monash University, Melbourne, Australia), Julien Hubert (Progress Technologies, Tokyo, Japan), Jitka Cejkova (University of Chemistry and Technology, Prague, Czech Republic), Steen Rasmussen (University of Southern Denmark & Santa Fe Institute, NM, USA), Manuel Baltieri (Riken, Tokyo, Japan), Antoine Pasquali (Cross Labs, Tokyo, Japan)
    Workshop overview: The purpose of this workshop is to discuss how Artificial Life research can benefit both human society and enhance the life of all organisms on the planet. The session will invite all participants to discuss the initiation of new means to encourage Artificial Life research towards sustained, positive impact with the potential to be felt beyond the field. This will be addressed via an open-ended discussion that begins to set objectives for consideration as end-goals, and for describing means by which progress towards the goals can be monitored or assessed.

    Twitter Handle: @ALife4Good


    Developmental Neural Networks Workshop (DevoNN)

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    Alife Slack Channel: #workshop-DevoNN

    Keywords: Development, Neural Networks, Artificial Intelligence, Neuroscience, Developmental Biology, Neurogenesis, Neuronal process, Synaptogenesis, Synaptic pruning


    Organizers: Julian F. Miller, Sylvain Cussat-Blanc, Dennis G. Wilson
    Workshop Description: In nature, brains are built through a process of biological development where structural aspects of the network change while learning. Incorporating development into ANNs raises fundamental questions, such as the balance between structural learning and synaptic learning. In this workshop, we will focus on the questions of neural development, an area that is under-explored in contemporary ANN literature.

    Lifelike Computing Systems Workshop (LIFELIKE)

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    Alife Slack Channel: #workshop-LIFELIKE

    Keywords: self-x properties, self-organization, self-explainability, self-improvement, self-awareness, autonomous learning, resilience, flexibility, organic computing

    Organizers: Anthony Stein, Sven Tomforde, Jean Botev, Peter Lewis
    The LIFELIKE Computing Systems Workshop is intended to provide a forum for discussing new insights and implications from adopting Artificial Life principles to technical computing systems deployed to flexibly and robustly act in real-world environments. Thereby, we explicitly emphasize the aspects of interpretability and explainability of the involved algorithms in order to provide a basis for system transparency already at the core of its mechanisms. Besides this self-explanatory property, further key ingredients to reach a specific level of intelligence are self-awareness and the resulting ongoing pursuit for continual self-improvement by means of learning and optimization. The resulting particular tension between increasing system viability through adopting life-like characteristics, while at the same time ensuring an appropriate degree of system explainability, validation and compliance to exploration boundaries, constitutes the main motivation and unique topic of LIFELIKE.

    Twitter: @lifelikecs


    Symbiosis in Artificial Life (SAL)

    Alife Slack Channel: #workshop-SAL

    Keywords: symbiosis, artificial life, mutualism, parasitism, evolution, commensalism

    Organizers: Anya E. Vostinar, Erik Hom, Luis Zaman
    Symbiosis, a close and long-term interaction between two or more organisms from different species, is a ubiquitous phenomenon, found at all levels of life and nearly every context that we have looked for it. This workshop aims to connect researchers interested in understanding symbiosis through artificial life by presenting the currently available models and discussing challenges, potential solutions, and next steps for the field.


    Teaching with Artificial Life (TAL)

    Alife Slack Channel: #workshop-TAL

    Keywords: education, artificial life, evolution education, computer science education, object-oriented programming, experiential learning

    Organizers: Anya E. Vostinar, Barbara Z. Johnson, Michael Wiser
    Because Artificial Life spans the fields of computer science and biology, it offers a unique perspective on the education of novices in both of those fields. This workshop aims to bring together educators and researchers in artificial life to discuss the many ways in which artificial life can be used to teach both biology and computer science.


    The First Proteus Workshop

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    Alife Slack Channel: #workshop-proteus

    Keywords: evolutionary algorithms, bio-inspired artificial intelligence, crowdsourcing, robotics, wet ALife

    Organizers: Josh Bongard, Nick Cheney, Melanie Moses
    The Proteus project is a combined evolutionary robotics / crowdsourcing effort. Researchers may write biological mechanisms as software patches, to be incorporated into agents continually evolving on the cloud.

    Twitter: @DoctorJosh


    The Second International Workshop on Agent-Based Modelling for Human Behaviour (ABMHuB)

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    Alife Slack Channel: #workshop-ABMHuB

    Keywords: Agent-based modelling Behavioural science Communication Economy Logistics Learning Social networks Collective intelligence Emergent behaviour


    Organizers: Katarzyna (Kasia) Kozdon, Soo Ling Lim, Peter J. Bentley
    International Workshop on Agent-Based Modelling of Human behaviour brings together modelling and behavioural science to address questions related but not restricted to communication, teamwork, learning and emergent behaviours in fields such as economy, logistics and sociology.

    Twitter: #ALifeBehavior2020


    Workshop on the synthesis of affectivity, embodiment and AI (Affectivity & AI)

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    Alife Slack Channel: #workshop-AffectivityAI

    Keywords: Affectivity, ALife, Artificial Intelligence, Embodiment, Emotions, Affective Computing, 4E Cognition, Human-Machine Interaction (HMI)

    Organizers: Jesús M. Siqueiros; Laura Mojica; Melina Gastelum
    What, if any, could be the role of embodiment in the AI/ALife synthesis of affectivity? This question leads our workshop. We take it as an opportunity to clarify what is meant conceptually and operationally by the 4e cognition claim that cognition is constitutively embodied and affective.

    Twitter: @Affectivity_Ai


    Interdisciplinary Approaches to A-Life and the Digital Arts (A-Life and the Arts)

    Alife Slack Channel: #workshop-ALifeandtheArts

    Keywords: interaction; machinic; dynamic media; machine learning; interaction; evolution; temporal shape

    Organizers: Chris Salter, Takashi Ikegami, Alexandre Saunier, Sofian Audry
    This hands on workshop explores the use of A-Life techniques in an area of growing research and creation practice: the new conception and design of complex interactive machines/ media environments for the arts as well as entertainment contexts.

    Twitter hashtag: #alifearts

  • SPECIAL SESSIONS
    Hybrid Life III: Approaches to integrate biological, artificial and cognitive systems (Hybrid Life III)

    Alife Slack Channel: #special-HybridLifeIII

    Keywords: Dynamical systems theory, Stochastic optimal control, Cognitive robotics, Life-mind continuity thesis, Systems biology, Animal-robot interaction, Bio-inspired robotics, Bio-integrated robotics, Human-machine interaction, Augmented cognition


    Organizers: Manuel Baltieri, Keisuke Suzuki, Hiroyuki Iizuka, Olaf Witkowski, Lana Sinapayen
    Hybrid life III focuses on the use of unifying mathematical frameworks, composite (artificial+biological) architectures and coupled systems for cognition to address highly cross-disciplinary topics at the intersection of cognitive science, artificial systems and biology.

    The Bibites : Getting the online community involved into Alife through game development (Bibites)

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    Alife Slack Channel: #special-Bibites

    Keywords: YouTube, simulation, continuous project

    Organizers: Léo Causssan
    Alife projects tend to be one-shots, released at once and then left aside. What is there to gain from continuing a project and building a community around it ?

    Twitter: @TBibites


    ALIFE and Society

    Alife Slack Channel: #special-ALIFEandSociety

    Keywords: ALIFE/AI and societal challenges; Socio-Ecological-technical systems; steering complex systems; philosophy; ethics; politics; social sciences; ALIFE for the Anthropocene; synthetic biology/ecology; hybrid, living & lifelike complex systems; Co-evolution of self, society, biosphere/technology;

    Organizers: Alex Penn, JM Siqueiros
    A forum to discuss grand challenges within human socio-ecological-technical systems: ALIFE/AI for society; Philosophical, ethical & social issues; designing/steering hybrid, living and lifelike CAS; ALIFE for the Anthropocene; future visions

  • TUTORIALS
    Functional Programming for Artificial Life (FPAL)

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    Alife Slack Channel: #tutorial-FPAL

    Keywords: functional programming, lambda calculus, Haskell, monad, combinator, artificial chemistry, artificial cell

    Organizers: Lance R. Williams
    An introduction to the Haskell programming language will be followed by an in depth look at a combinator-based artificial chemistry implemented as an embedded language inside Haskell, and the demonstration of a series of increasingly complex artificial organisms built using this chemistry. The unique features of Haskell that make this possible will be emphasized throughout.

    Twitter Hashtag: #FPAL


    Introduction to Artificial Gene Regulatory Networks (AGRN)

    Alife Slack Channel: #tutorial-AGRN

    Keywords: Artificial gene regulatory network Gene expression Developmental processes Models Applications in Artificial Life

    Organizers: Sylvain Cussat-Blanc; Wolfgang Banzhaf
    This tutorial will introduce the biology of gene regulatory networks, from the genetic aspect to the dynamics of gene regulation. We will show how bio-inspired models can lead to intelligent algorithms by showing various applications of artificial gene regulatory networks in Artificial Life.


    Large Scale Agent Based Modelling with FLAME GPU 2 (FLAME GPU 2)

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    Alife Slack Channel: #tutorial-FLAMEGPU2

    Keywords: Agent Based Modelling and Simulation, GPUs, large scale simulation

    Organizers: Paul Richmond
    Modelling and simulation of complex problems has become an established ‘third pillar’ of science, complementary to theory and experimentation. The multi-agent approach to modelling allows complex systems to be constructed in such a way as to add complexity from understanding at an individual level (i.e. a bottom-up approach). This approach is extremely powerful in a wide range of domains as diverse as computational biology to economics and physics. Whilst multi-agent modelling provides a natural and intuitive method to model systems the computational cost of performing large simulations is much greater than for top-down, system level alternatives. In order for multi-agent modelling and simulation to be used as a tool for delivering excellent science, it is vital that simulation performance can scale by targeting readily available computational resources effectively. In other words, increasing the size of the model or its accuracy directly impacts the amount of computational time required to perform the simulation. The use of parallel resources allows us to reduce time constraints and thus scale up the size and increase the accuracy of the model . FLAME GPU 2 provides this computational capacity by targeting readily available Graphics Processing Units capable of simulating many millions of interacting agents with performance which exceeds that of traditional CPU based simulators. FLAME GPU 2 is the next generation of the FLAME GPU software, developed in the UK since 2008 and delivered as a tutorial at ALIFE in 2018 and 2019. FLAME GPU 2 is an agent-based modelling simulation platform that enables modellers from various disciplines like economics, biology and social sciences to easily write agent-based models. Importantly, it abstracts the complexities of the GPU architecture away from modellers to ensure that modellers can concentrate on writing models without the need to acquire specialist knowledge typically required to utilise GPU architectures. This tutorial is aimed at the intermediate level. No knowledge of GPUs is required however basic knowledge multi agent modelling approaches is expected (i.e. formulating a problem as a set of individuals within a system) as well as a basic programming ability. The tutorial format is an introductory lecture followed by participants being provided the opportunity to complete a hands on exercise using the FLAME GPU software. Participants will be provided dedicated GPU cloud resources for interactively configuring and running a large scale agent based model within FLAME GPU. By the end of the practical session, it is expected that the participants will understand how to write and execute a multi-agent model for FLAME GPU from scratch. Participants will leave with an appreciation of the key techniques, concepts, and algorithms which have been used.


    Molecular programming of swarms for ALife (MolProg)

    Alife Slack Channel: #tutorial-MolProg

    Keywords: Molecular programming Swarming Molecular robots Evolutionary optimization Reality gap In-vitro implementation

    Organizers: Nathanael Aubert-Kato, Leo Cazenille, Nicolas Lobato-Dauzier
    This tutorial will introduce molecular programming tools to implement a variety of in-vitro systems, ranging from arbitrary chemical reaction networks to swarms with more than a million individual units.


    Programming soft alife with SPLAT and ulam (HackSPLAT)

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    Alife Slack Channel: #tutorial-HackSPLAT

    Keywords: Soft alife, programming, robust-first computing, best-effort architecture, indefinite scalability, Movable Feast Machine, living computation, SPLAT, ulam

    Organizers: Dave Ackley
    Deterministic CPU/RAM computing is becoming unscalable and unsecurable. The Movable Feast Machine is an indefinitely scalable architecture based on best-effort correctness and robust, life-like software. This tutorial introduces the two main programming languages for the MFM.


    Tracing epidemics with agent-based and network based models (EpiAgeNet)

    Alife Slack Channel: #tutorial-EpiAgeNet

    Keywords: computational epidemiology, agent-based modeling, complex networks, pandemics, epidemics, ACEMod, spatiotemporal spread, molecular genotyping, evolution, small-world

    Organizers: Mikhail Prokopenko
    We will consider pros and cons of agent-based and network-based models for studying epidemics, tracing a spatiotemporal spread of epidemics across a nation. We will also explore how high-resolution molecular genotyping data can help to infer weighted genetic networks, tracing emergence and evolution of dominant strains.


    Visualization Principles and Techniques for Research in ALife (ALifeVis2020)

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    Alife Slack Channel: #tutorial-ALifeVis2020

    Keywords: information visualization, multidimensional data, network data, time-varying data

    Organizers: Michael McGuffin
    A tutorial on basic principles for designing charts and visualizations; example strategies for visualizing multidimensional data, network data, time-varying data; brief critique of example visualizations drawn from ALife literature.

    Twitter Handle: @MJMcGuffin


    Introduction to MABE, A tool for studying evolving systems and digital brains (MABE)

    Alife Slack Channel: #tutorial-MABE

    Keywords: Research Tool, Evolution, Neuro Evolution, Simulation

    Organizers: Clifford Bohm
    MABE is a modular agent-based digital evolution research tool that can quickly get you from hypothesis to results. In this tutorial, you can learn the basics of MABE and how you can start to use it.


    Cartesian Genetic Programming (CGP)

    Alife Slack Channel: #tutorial-CGP

    Keywords: Genetic programming, evolutionary algorithms, graphs, circuits, image processing, neural networks, artificial development

    Organizers: Julian Miller
    Cartesian Genetic Programming uses evolutionary algorithms to create graphs. These can represent many things (e.g. circuits, equations, neural networks). It can be a useful tool for creating the programs running inside artificial cells and neurons. It is widely applicable to many computational problems. It is easy to program.

  • UNCONFERENCE SESSIONS
    Robotics Demo Session

    Alife Slack Channel: #robots

    Keywords: robotics

    Organizers: Sam Kriegman


    Open AI Gym Hackathon

    Alife Slack Channel: #hackathon

    Keywords: Open AI Gym

    Organizers: Jack Felag