Barry McMullin

John von Neumann and the evolutionary growth of complexity: looking backward, looking forward …

In the late 1940s John von Neumann began to work on what he intended as a comprehensive theory of [complex] automata. He started to develop a book length manuscript on the subject in 1952. However, he put it aside in 1953, apparently due to pressure of other work. Due to his tragically early death in 1957, he was never to return to it. The draft manuscript was eventually edited, and combined for publication with some related lecture transcripts, by Burks in 1966. It is clear from the time and effort that von Neumann invested in it that he considered this to be a very significant and substantial piece of work. However, subsequent commentators (beginning even with Burks) have found it surprisingly difficult to articulate this substance. Indeed, it has since been suggested that von Neumanns results in this area either are trivial, or, at the very least, could have been achieved by much simpler means. It is an enigma. In this paper I review the history of this debate (briefly) and then present my own attempt at resolving the issue by focusing on an analysis of von Neumanns problem situation. I claim that this reveals the true depth of von Neumanns achievement and influence on the subsequent development of this field, and further that it generates a whole family of new consequent problems, which can still serve to informif not actually definethe field of artificial life for many years to come.

Thirty Years of Computational Autopoiesis: A Review

Computational autopoiesisthe realization of autopoietic entities in computational mediaholds an important and distinctive role within the field of artificial life. Its earliest formulation by Francisco Varela, Humberto Maturana, and Ricardo Uribe was seminal in demonstrating the use of an artificial, computational medium to explore the most basic question of the abstract nature of living systemsover a decade in advance of the first Santa Fe Workshop on Artificial Life. The research program it originated has generated substantive demonstrations of progressively richer, lifelike phenomena. It has also sharply illuminated both conceptual and methodological problems in the field. This article provides an integrative overview of the sometimes disparate work in this area, and argues that computational autopoiesis continues to provide an effective framework for addressing key open problems in artificial life.

Towards the Implementation of Evolving Autopoietic Artificial Agents

We report modifications to the SCL model[2], an artificial chemistry in Swarm designed to support autopoietic agents. The aim of these modifications is to improve on the longevity of the agents and to implement growth. We demonstrate by means of two simulation runs that the improvements indeed have the desired effects.

Is it the right ansatz

This article is a response to Rasmussen et al. [Artificial Life, 7, 329350], in which the authors suggest that, within a particular simulation framework, there is a tight correspondence between the complexity of the primitive objects and the emergence of dynamical hierarchies. As an example they report a two-dimensional artificial chemistry that supports the spontaneous emergence of micellar structures, which they classify as third-order structures. We report in this article that essentially comparable phenomena can be produced with relatively simpler primitive objects. We also question the order classification of the micellar structures.

Evolving Artificial Cell Signaling Networks: Perspectives and Methods

Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. In this paper we introduce an abstraction of Cell Signaling Networks focusing on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. Following this we describe a novel class of Artificial Chemistry named Molecular Classifier Systems (MCS) to simulate ACSNs. The MCS can be regarded as a special purpose derivation of Hollands Learning Classifier System (LCS). We propose an instance of the MCS called the MCS.b that extends the precursor of the LCS: the broadcast language. We believe the MCS.b can offer a general purpose tool that can assist in the study of real CSNs in Silico The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs.

Defining and simulating open-ended novelty: requirements, guidelines, and challenges

The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system’s model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community.

Remarks on autocatalysis and autopoiesis.

Abstract: The notions of collective autocatalysis and of autopoiesis are clearly related; equally clearly, they are not quite the same. The purpose of this paper is to try to clarify the relationship. Specifically I suggest that autopoiesis can be at least roughly characterized as collective autocatalysis plus spatial individuation. Although some mechanism of spatial confinement or concentration is probably necessary to the effective operation of any collectively autocatalytic reaction network, autopoiesis requires, in addition, that the mechanism for maintaining this confinement should itself be a product of the reaction network‐and should thus (?) be capable of separating or individuating otherwise identically organized networks. I suggest an informal heuristic test to discriminate the (merely) collectively autocatalytic from the (properly) autopoietic. Finally, in the light of this, I review a variety of published abstract or model systems, Alchemy, α‐universes, Tierra, and SCL.

Open-ended evolution: Perspectives from the oee workshop in york

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshops talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1.

Architectures for Self-reproduction: Abstractions, Realisations and a Research Program

It is well recognised that von Neumann’s seminal abstraction of machine self-reproduction can be related to the reality of biological self-reproduction — albeit only in very general terms. On the other hand, the most thoroughly studied artificial evolutionary systems, incorporating meaningful selfreproduction, are the coreworld systems such as Tierra, Avida etc.; and these, in general, rely on a purely “self-inspection” mode of reproduction (or, more simply, “replication”). To the extent that the latter has any direct biological analog it would appear to be with molecular level reproduction and evolution in the hypothesised RNA-world. In this paper I review the details and distinctions between these modes of reproduction. I indicate how the abstract von Neumann architecture can, in fact, be readily realised in coreworld systems; and outline the research program that flows from this. Finally I attempt to make more precise the resulting analogies with molecular biology, at least up to the (prokaryotic) cell level.

The Emergence of Pathological Constructors When Implementing the von Neumann Architecture for Self-reproduction in Tierra

John von Neumann’s architecture for genetic reproduction provides an explanation in principle for how arbitrarily complex machines can construct other (“offspring”) machines of equal or even greater complexity. We designed a von Neumann style self-reproducing ancestor, within the framework of the Tierra platform, which implements a (mutable) genotype-phenotype mapping during reproduction. However, we have consistently observed a particular phenomenon where what we call pathological constructors quickly emerge, which ultimately lead to catastrophic ecosystem collapse. Pathological constructors are creatures which rapidly construct multiple short malfunctioning offspring within their lifetime. Pathological constructors are a hindrance to an ecosystem because their offspring, although sterile, still occupy both memory space and CPU time. When several pathological constructors coincide in time, their production rate can be so high that their non-functional offspring displace the entire population of functional self-reproducing creatures, resulting in ecosystem collapse. We investigate the origin of pathological constructors, and consider how a more mutational robust architecture which is less susceptible to the emergence of these creatures can be