Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.
This volume presents the proceedings of ALIFE 2018, the 2018 Conference on Artificial Life, held July 23rd-27th. It took place in Tokyo, Japan (http://2018.alife.org). The ALIFE and ECAL conferences have been the major meeting of the artificial life (ALife) research community since 1987 and 1991, respectively. As a Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE), the 2018 Conference on Artificial Life (ALIFE 2018) will take place outside both Europe and the US, in Tokyo, Japan.
The ALife conferences are the major meeting of the artificial life research community since 1987. For its 15th edition in 2016, it was held in Latin America for the first time, in the Mayan Riviera, Mexico, from July 4 -8. The special them of the conference: How can the synthetic study of living systems contribute to societies: scientifically, technically, and culturally? The goal of the conference theme is to better understand societies with the purpose of using this understanding for a more efficient management and development of social systems.
A review of Summa Technologiae by Stanislaw Lem by David Auerbach from the Los Angeles Review of Books.
Summa Technologiae
AT LAST WE have it in English. Summa Technologiae, originally published in Polish in 1964, is the cornerstone of Stanislaw Lem’s oeuvre, his consummate work of speculative nonfiction. Trained in medicine and biology, Lem synthesizes the current science of the day in ways far ahead of most science fiction of the time.
I came across this book review quite serendipitously today via an Auerbach article in Slate, which I’ve bookmarked. I found a copy of the book and have added it to the top of my reading pile. As I’m currently reading an advance reader edition of Sean Carroll’sThe Big Picture, I can only imagine how well the two may go together despite being written nearly 60 years apart.
“My vision of life is that everything extends from replicators, which are in practice DNA molecules on this planet. The replicators reach out into the world to influence their own probability of being passed on. Mostly they don’t reach further than the individual body in which they sit, but that’s a matter of practice, not a matter of principle. The individual organism can be defined as that set of phenotypic products which have a single route of exit of the genes into the future. That’s not true of the cuckoo/reed warbler case, but it is true of ordinary animal bodies. So the organism, the individual organism, is a deeply salient unit. It’s a unit of selection in the sense that I call a “vehicle”. There are two kinds of unit of selection. The difference is a semantic one. They’re both units of selection, but one is the replicator, and what it does is get itself copied. So more and more copies of itself go into the world. The other kind of unit is the vehicle. It doesn’t get itself copied. What it does is work to copy the replicators which have come down to it through the generations, and which it’s going to pass on to future generations. So we have this individual replicator dichotomy. They’re both units of selection, but in different senses. It’s important to understand that they are different senses.”
Richard Dawkins
RICHARD DAWKINS is an evolutionary biologist; Emeritus Charles Simonyi Professor of the Public Understanding of Science, Oxford; Author, The Selfish Gene; The Extended Phenotype; Climbing Mount Improbable; The God Delusion; An Appetite For Wonder; and (forthcoming) A Brief Candle In The Dark.
Watch the entire video interview and read the transcript at Edge.org.
Artificial Life aims to understand the basic and generic principles of life, and demonstrate this understanding by producing life-like systems based on those principles. In recent years, with the advent of the information age, and the widespread acceptance of information technology, our view of life has changed. Ideas such as “life is information processing” or “information holds the key to understanding life” have become more common. But what can information, or more formally Information Theory, offer to Artificial Life?
One relevant area is the motivation of behaviour for artificial agents, both virtual and real. Instead of learning to perform a specific task, informational measures can be used to define concepts such as boredom, empowerment or the ability to predict one’s own future. Intrinsic motivations derived from these concepts allow us to generate behaviour, ideally from an embodied and enactive perspective, which are based on basic but generic principles. The key questions here are: “What are the important intrinsic motivations a living agent has, and what behaviour can be produced by them?”
Related to an agent’s behaviour is also the question on how and where the necessary computation to realise this behaviour is performed. Can information be used to quantify the morphological computation of an embodied agent and to what degree are the computational limitations of an agent influencing its behaviour?
Another area of interest is the guidance of artificial evolution or adaptation. Assuming it is true that an agent wants to optimise its information processing, possibly obtain as much relevant information as possible for the cheapest computational cost, then what behaviour would naturally follow from that? Can the development of social interaction or collective phenomena be motivated by an informational gradient? Furthermore, evolution itself can be seen as a process in which an agent population obtains information from the environment, which begs the question of how this can be quantified, and how systems would adapt to maximise this information?
The common theme in those different scenarios is the identification and quantification of driving forces behind evolution, learning, behaviour and other crucial processes of life, in the hope that the implementation or optimisation of these measurements will allow us to construct life-like systems.
According to their release, the open access journal Entropywill sponsor the workshop by an open call with a special issue on the topic of the workshop. More details will be announced to emails received via itialife@gmail.com and over the alife and connectionists mailing lists.
