ALife 2020

tags: ALife Conference, Artificial life

Day 1

Tutorial - Functional programming for artificial life

Tutorial - Visualization Principles and Techniques for Research in ALife

Day 2

Sara Walker, keynote Lecture

About what life means and how it can be defined from the point of view of physics/information theory, etc.

Melanie Mitchell, keynote Lecture

This talk was very similar to another one I watched from Santa Fe Institute which promotes her book: Artificial Intelligence: A Guide for Thinking Humans.

It gives a detailed breakdown of why machine learning still fails at many tasks and might not be able to overcome them before we change the current way of doing it. The main takeaway for me is that Mitchell believes we will need to somehow build abstraction or conceptualization capabilities within our ML models to make them able to use “common sense” and understand things we consider as basic.

Day 3

Lee Cronin, keynote Lecture

Contributed session 1

Djordje Grbic, Sebastian Risi - Safe Reinforcement Learning through Meta-learned Instincts

See notes (Grbic, Risi 2020).

Toby Howison, Josie Hughes, Fumiya Iida - Morphologically programming the interactions of V-shaped falling papers

Interesting talk about falling V-shaped papers. The range of exhibited behavior is surprising and is an instance of complex systems with a few controllable parameters but surprising dynamics.

Cem Tutum, Risto Miikkulainen - Adapting to Unseen Environments through Explicit Representation of Context

See notes (Tutum, Miikkulainen 2020).

Manon Flageat, Antoine Cully - Fast and stable MAP-Elites in noisy domains using deep grids

See notes (Flageat, Cully 2020).

Julian F. Miller: Evolving developmental neural networks to solve multiple problems

This talk is about introducing a new model of neural network neuron that could have enough modularity to deal with multiple problems at once.

Socials institutions can be seen as large games with rules, norms, and values. They are complex evolving systems starting form simple cooperation and evolving to complex things. The author applies a game theory framework to these systems.

James M. Borg, Matt Grove, Fiona Polack - Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems

Many biological time-series exhibit a type of colored noise. The authors use different noise functions in evolutionary models and observe that it profoundly affect the evolutionary response. This is interesting because it shows that the most often used white noise (Gaussian noise) might not be the best choice for evolution simulations.

Contributed session 2

Randall D. Beer - An Integrated Perspective on the Constitutive and Interactive Dimensions of Autonomy

See notes (Beer 2020).

Mark Bedau - Dynamic natural kinds: Open-ended evolution in the joints of nature

The authors use word2vec on patents to identify technological feature spaces. The similarity between patent descriptions is used to measure their proximity. This offers and interesting perspective on the space of different technologies and their hierarchical and dynamical properties.

Acacia Ackles, Austin Ferguson, Connor Grady, Charles Ofria - Rank epistasis: A new model for analyzing epistatic interactions in the absence of quantifiable fitness interactions

This paper studies an epistasis model. The new proposed model is based on ranking loci of a genetic model based on their fitness. Then, after mutating a locus and computing the new ranking. By comparing statistical differences between the two rankings one can estimate the effect of the previously introduced mutation.

The authors used NK model to study the new metric in a relatively well studied environment. Sites with higher epistatic activity score higher according to their metric and this happens without any a priori information about how the sites interact with each other.

Thomas Helmuth, Edward Pantridge, Grace Woolson Lee Spector - Genetic Environment Sensitivity and Transfer Learning in Genetic Programming

This paper is about using genetic programming for program synthesis.

Mahi Luthra, Eduardo J. Izquierdo, Peter M. Todd - Cognition Evolves with the Emergence of Environmental Patchiness

Using a simple agent based simulation of animals and plants in a grid world, the authors observe spontaneous “patchiness” of the partition of plants. Cognition, defined as the ability perceptual strength of an agent and the stochasticity of its decisions increases and stabilizes in those simulations.

When cognition is not restricted, patches are larger and more visible, whereas for restricted cognition resource distribution is more uniform. There is a two way influence of both parameters on the evolution of patches and cognition.

Lauren Benso, Lauren Benson, Madhavun Candadai and Eduardo Izquierdo - Neural reuse in multifunctional circuits for control tasks

This talk is about studying a simple two-layers feedforward neural network evolved to solve three RL tasks at the same time. Then by introducing neuron lesions and their effect on the fitness, one is able to identify the useful neurons and their importance.

Many neurons are re-used across multiple tasks. With neural variance and mutual information within each neurons the authors are able to analyze the reuse of neurons.

The takeaways are :

It seems relatively simple to obtain neuron reuse across tasks with simple evolutionary methods
Successful neural networks on theses toy tasks appear to reuse their neurons

Contributed session 3

Shane St. Luce, Hiroki Sayama - Phase Spaces of the Strategy Evolution in the El Farol Bar Problem

Guillaume St-Onge, Antoine Allard, Laurent Hébert-Dufresne - Localization, bistability and optimal seeding of contagions on higher-order networks

In this talk, the author studies higher-order networks, which differently from simple networks, have interactions that can exist within groups of elements (instead of pairs). This allows studying contagion and spreading dynamics from a new angle.

Juan Perez-Mercader - Non-biochemical autonomous synthesis and boot-up from a chemical homogeneous mixture of a population of vesicles: their birth, growth, self-replication, extinction and competition cycles

Abe Leite, Madhavun Candadai and Eduardo J. Izquierdo - Reinforcement learning beyond the Bellman equation: Exploring critic objectives using evolution

Thomas Helmuth, Lee Spector - Explaining and Exploiting the Advantages of Down-sampled Lexicase Selection

Oskar Elek, Joseph N. Burchett, J. Xavier Prochaska and Angus G. Forbes - Monte Carlo Physarum Machine: Agent-based Model for Reconstructing Complex 3D Transport Networks

Cosmic web is diffuse, not only composed of galaxies but hot and cold gas and dark matter. It is very hard to capture from experimental data. Authors use the slime mold as a model for reconstructing this network. This mold is interesting because by using gradients of chemicals emitted by food and itself, it can construct near-optimal transport networks. Their model is also inspired from Sage Jensons’s cyber critters adapted to 3D. They use Monte-carlo sampling of directions around the gradient for moving each tiny agent which results in more natural-looking simulations.

This model reproduces the cosmic web in a relatively faithful way visually, and is robust thanks to its stochastic component.

Contributed session 4

Matthew Egbert - Marangoni Based Motile Oil-Droplets in Simulated Artificial Chemistry

Nicolas Lobato-Dauzier, Leo Cazenille , Teruo Fujii, Anthony Genot and Nathanael Aubert-Kato -Temperature-based inputs for molecular reservoir computers

Azumi Mamiya, Genki Ichinose - Zero-Determinant Strategies under Observation Errors

Ekaterina Sangati, Simon M. Hofmann - The Role of Co-Representations in Joint Tracking

Palin Choviwatana, Shota Ejima, Mizuki Oka, Takashi Ikegami - Web as an Evolutionary Ecosystem: Emergence of Keystone Species

Contributed session 5

Hoang Nguyen, Peter Banda, Darko Stefanovic, Christof Teuscher - Reservoir Computing with Random Chemical Systems

Michael L Wong and Stuart Bartlett - Defining Lyfe in the Universe: From Three Privileged Functions to Four Pillars

Manh Hong Duong, The Anh Han - The effect of mutation on equilibrium properties of deterministic and random evolutionary games

Poster Session

Matthew Dale - Designing Computational Substrates using Open-Ended Evolution

This poster is about characterizing the computational capabilities of reservoir computing systems. The author’s goal is to design computational substrates that have interesting properties when used as reservoir computing basis.

For this, the authors have to define a behavior space that they slice up into voxels. They use novelty search to explore that space and measure the quality of the possible behavior.

Elissa Cohen - Resilient Life: An Exploration of Perturbed Autopoietic Patterns in Conway’s Game of Life

See notes (Cika et al. 2020).

Day 4

Luis Zaman, keynote Lecture

Contributed session 6

Contributed session 7

Day 5

Contributed session 8

Contributed session 9

Day 6

Contributed session 10

Demyan Vakhrameev, Xabier Barandiaran, Miguel Aguilera, Manuel Bedia - Measuring Autonomy for Life-Like AI

Léni K. Le Goff, Emma Hart, Agoston E. Eiben - Sample and time-efficient policy learning with CMA-ES and Bayesian Optimisation

Paul Ecoffet, Jean-Baptiste André, Nicolas Bredeche - Learning to Cooperate in a Socially Optimal Way in Swarm Robotics

Payam Zahadat, Ada Diaconescu - Reactive or Stable: A Plant-inspired Approach for Organisation Morphogenesis

Edmund Hunt, Nigel R. Franks, Roland J. Baddeley - The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems

Tim Taylor - The Importance of Open-Endedness (for the Sake of Open-Endedness)

Asheesh Sharma, Sabine Hauert and Helmut Hauser - Morphological communication for swarms

Contributed session 11

Gunnar Tufte, Johannes H. Jensen - Reservoir Computing in Artificial Spin Ice

See notes.

Stephan Scheidegger, Alexander Mikos, Harold Fellermann - Modelling Artificial Immune – Tumor Ecosystem Interaction During Radiation Therapy Using a Perceptron – Based Antigen Pattern Recognition

Juste Raimbault - A model of urban evolution based on innovation diffusion