For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into 14 themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields. Relevance: Artificial life has contributed to philosophy of biology and of cognitive science, thus making it an important field related to constructivism.

We present a tentative proposal for a quantitative measure of autonomy. This is something that, surprisingly, is rarely found in the literature, even though autonomy is considered to be a basic concept in many disciplines, including artificial life. We work in an information theoretic setting for which the distinction between system and environment is the starting point. As a first measure for autonomy, we propose the conditional mutual information between consecutive states of the system conditioned on the history of the environment. This works well when the system cannot influence the environment at all and the environment does not interact synergetically with the system. When, in contrast, the system has full control over its environment, we should instead neglect the environment history and simply take the mutual information between consecutive system states as a measure of autonomy. In the case of mutual interaction between system and environment there remains an ambiguity regarding whether system or environment has caused observed correlations. If the interaction structure of the system is known, we define a “causal” autonomy measure which allows this ambiguity to be resolved. Synergetic interactions still pose a problem since in this case causation cannot be attributed to the system or the environment alone. Moreover, our analysis reveals some subtle facets of the concept of autonomy, in particular with respect to the seemingly innocent system–environment distinction we took for granted, and raises the issue of the attribution of control, i.e. the responsibility for observed effects. To further explore these issues, we evaluate our autonomy measure for simple automata, an agent moving in space, gliders in the game of life, and the tessellation automaton for autopoiesis of Varela et al.

In this article, we would like to discuss some aspects of a theoretical framework for Artificial Life, focusing on the problem of an explicit definition of living systems useful for an effective artificial construction of them. The limits of a descriptive approach will be critically discussed, and a constructive (synthetic) approach will be proposed on the basis of the autopoietic theory of Maturana and Varela.

Key words: self-organization, autopoiesis, biological autonomy, artificial life, constructivism

In the late 1950's Gordon Pask constructed several electrochemical devices having emergent sensory capabilities. These control systems possessed the ability to adaptively construct their own sensors, thereby choosing the relationship between their internal states and the world at large. Devices were built that evolved de novo sensitivity to sound or magnetic fields. Pask’s devices have far-reaching implications for artificial intelligence, self-constructing devices, theories of observers and epistemically-autonomous agents, theories of functional emergence, machine creativity, and the limits of contemporary machine learning paradigms.

Key words: Epistemology, cybernetics, self-organizing systems, emergence, sensory evolution, machine creativity, evolutionary robotics, artificial intelligence, artificial life, autonomous agents.

Open peer commentary on the article “Exploration of the Functional Properties of Interaction: Computer Models and Pointers for Theory” by Etienne B. Roesch, Matthew Spencer, Slawomir J. Nasuto, Thomas Tanay & J. Mark Bishop. Upshot: Artificial life computer simulations hold the potential for demonstrating the kinds of bottom-up, cooperative, self-organizing processes that underlie the self-construction of observer-actors. This is a worthwhile, if limited, attempt to use such simulations to address this set of core constructivist concerns. Although we concur with much of the philosophical perspective in the target article, we take issue with some of the implied positions related to dynamical systems, sensorimotor contingency theory, and neural information processing. Ideally, we would like to see computational approaches more directly address adaptive, constructive processes and mechanisms operant in minds and brains. This would entail using tasks that are more relevant to the psychology of human and animal learning than performing digit sums or sorts. It also could involve relating the dynamics of agents more explicitly to ensembles of communicating neural assemblies.

In this paper we present a new model for the mechanism underlying what is traditionally known in immunology as the “selfnonself” distinction. It turns out that in operational terms, the distinction effected by this model of the immune system is between a sufficiently numerous set of antigens present from the start of the ontogeny of the system on the one hand, and isolated antigens first introduced after the system has reached maturity on the other. The coincidence between this “founder versus late” distinction and the traditional “somatic self-foreign pathogen” one is essentially contingent, an example of the purely opportunistic tinkering characteristic of biological organization in general. We conclude that the so-called “self-nonself” distinction in immunology is a misleading misnomer. This raises the question as to what would genuinely count as a “self-nonself” distinction, a fundamental question for biology in general and Artificial Life in particular.

Review of: Andy Clark (1997) Being There: Putting Brain, Body and World Together Again. MIT Press: Cambridge, Massachusetts. xiii+267pp. ISBN 0-262-03240-6. Price: $US12 pbk.

Key words: artificial intelligence, dynamical systems theory, embodiment, autonomous agents, artificial life, mental repesentation, metaphysics.

A novel scenario is emerging from the synthetic biology advancements of the last fifteen years. We refer to a well-defined multidisciplinary sci-tech arena dedicated to the construction of biological-like systems, and, in particular, microscopic cell-like systems. The challenge of assembling a minimal cell from separated parts is generally considered the Holy Grail of biology. However, an accurate analysis of this emerging line of research, grounded in the theory of autopoiesis and its implications, is able to show its potentially high relevance for two other fields – artificial life and artificial intelligence. In this paper we intend to propose this perspective. Based on the critical discussion of recent trends and experimental results in synthetic biology, we sketch out how current research in this field can impact not only artificial life, but also artificial intelligence inquiries, in particular with respect to embodied cognition.

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however, resolve that of the relevance of the meaning created by the machine (sense-making) Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways.

Key words: enaction, embodied-embedded ai, sense-making, co-evolution, guidance, phylogenesis/ontogenesis ratio, human in the loop, virtual reality

This article focuses on an artificial life approach to some important problems in machine learning such as statistical discrimination, curve approximation, and pattern recognition. We describe a family of models, collectively referred to as semi-algebraic networks (SAN). These models are strongly inspired by two complementary lines of thought: the biological concept of autopoiesis and morphodynamical notions in mathematics. Mathematically defined as semi-algebraic sets, SANs involve geometric components that are submitted to two coupled processes: (a) the adjustment of the components (under the action of the learning examples), and (b) the regeneration of new components. Several examples of SANs are described, using different types of components. The geometric nature of SANs gives new possibilities for solving the bias/variance dilemma in discrimination or curve approximation problems. The question of building multilevel semi-algebraic networks is also addressed, as they are related to cognitive problems such as memory and morphological categorization. We describe an example of such multilevel models.