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Imagine evolving swarms of robots interacting and by doing so reshaping and cultivating our habitat. This habitat could be here on Earth, on a distant planet or moon, or within a self-contained spacecraft. What would these robots look like and made of what type of material? What kind of information, hardware or software? What are the architectural necessities? There are many open questions when trying to envision the future of architecture; but, in this particular workshop, the goal was not only to imagine the future but also to create it. With this particular goal in mind, the Living Architecture workshop at European Conference on Artificial Life (ECAL) 2017 brought together practitioners from the sciences and architecture to share ideas and technologies to examine possible paths forward. Living Architecture is a specific substantiation of the broader notion of Living Technology where living systems or artificial systems with life-like properties are developed towards technological applications. In Living Architecture, objects designed in the built environment would contain living systems as part of their functionality (such as bioreactors for energy) or artificial distributed systems with feedback loops (such as neural networks or artificial intelligence). In this way, Living Architecture represents a congruence in functionality and form between living systems, technology and architecture. [ABSTRACT FROM AUTHOR]

This report presents some highlights from ECAL 2017, the Fourteenth European Conference on Artificial Life, which was held on 4–8 September 2017 in Lyon, France. [ABSTRACT FROM AUTHOR]

This article reports a study on modeling covert visual attention as a parallel process that unfolds in synergy with the embodied agent’s action selection process. The parallel nature of the proposed model is according to the multiple covert attention hypothesis and thus suitable for the concurrent search for multiple objects in the embodied agent’s visual field. In line with the active vision approach, the interaction with the action selection process is exploited by the model to deploy visual attention in a by-need way. Additionally, the multiple focuses of attention considered in the proposed model interact in a way that their collective behavior robustly self-organizes for a proper handling of the speed—accuracy trade-off inherent to visual search tasks. Besides the self-organization of a global spatiotemporal visual attention policy, the model also produces parallel, sparse, and active spatial working memories, that is, local maps of the environment. The underlying mechanisms of the model are based on the well known formalism that describes the self-organization of collective foraging strategies in social insects. This metaphor is particularly interesting because of its similarities with the problem tackled in this article, that is, the one of parallel visual attention. This claim is validated by experimental results on a simulated robot performing local navigation, where the ability of the model to generate accurate visual attention and spatial memories in a parsimonious and robust way is shown. [ABSTRACT FROM PUBLISHER]

This article describes and compares two approaches that can be used to build artificial systems that users tend to recognize as agents, based on a review of two systems previously built by the authors. One system, an interactive musical instrument, is a typical artificial intelligence (AI) implementation, based on the technique of constraint programming. The other, a dancing robot, is a typical artificial life (AL) endeavor, specified as a non-linear dynamical system. Although very different in their design, we found that both systems have the same goal of compromising autonomy and reactivity in their user interaction. They elicit interaction and—we argue here—an elusive feeling of agency, because they are neither too predictable nor too random. Creating and controlling such a compromise in a programmatic way is not a trivial problem: we find that both approaches (AI and AL) raise similar pragmatic problems that are in fact rooted in human perception science. Psychological experimentation is needed to clarify the relation between the internal dynamics of the artificial systems and the ongoing feeling of agency (or absence thereof) imparted in their human user. As a first step toward such experimentation, we derive a minimal mathematical model which subsumes both implementations and abstracts them from their respective contexts of music and dance. This model is similar to a Van der Pol oscillator, forced by an input signal coupled to its output in a non-programmatic way. It isolates two critical variables controlling the illusion of agency: the model's sampling rate and the polynomial order of its reactive term. [ABSTRACT FROM AUTHOR]

The article focuses on the quality of animat modeling approach in adaptive behavior (AB) to clarify the way that models can achieve greater biological relevance. It notes the models that explore how neural mechanism support a particular function including models of specific brain systems, sensory and motor coding in neural populations, and individual neurons. It mentions the potential of the empirical verifiability of models to be replaced in conflict with explanatory power.

The author comments on the perspective of researcher Barbara Webb on the need for animat research to seek more empirical validation which relates to her robotic modeling work on cricket phonotaxis. He notes the impact of Webb's plea regarding the exclusion of animat models from biology to extenuate the strategic problem related to the performance of acid test on an animat model. The presence of computational models in biology that do not suggest a concrete target organism is mentioned.

The article focuses on the responses to the article by Barbara Webb which relates to the restoration of biology as a natural history. It notes the comparison of two models by Webb's article that include one that is built with reference to a specific animal behavior and one that is built as an abstract possible for life. It suggests the need for an underlying theory for the creation of a comprehensive natural history for both real and artificial creatures.

The author comments on the perspective of researcher Barbara Webb regarding the purposefulness of exploratory tools and the legitimacy of biological models that are created to represent mechanisms. He notes a propagation of an error by Webb which similar to that of computer scientist who confuses models with reality. He stresses the significance of a tool to be appreciated with its use, its capacity to inspire new techniques, and its capability to lead to a creation.

The article discusses the relationship between adaptive behavior (AB) and artificial life (ALife) being both forms of modeling in reference to the study by Barbara Webb. It notes comments on various perspectives from modelers having interest with AB and ALife including the ability of ALife to use models and the denial of ALife on the study of real entities. The agreement to the quality of the research program of ALife to be incomplete and harmful for the study of biology is mentioned.

The author comments on the article of author Barbara Webb wherein she made contrast to the animal and animat models and their possession of standards of scientific relevance. He concurs to the engagement of the general issue of model interpretation in adaptive behavior and artificial life. He mentions the failure of Webb to recognize that there are many other kinds of modeling methodologies that provides different but equally valid answers to the questions related to the model's target.

The overlapping fields of adaptive behavior and artificial life are often described as novel approaches to biology. They focus attention on bottom-up explanations and how lifelike phenomena can result from relatively simple systems interacting dynamically with their environments. They are also characterized by the use of synthetic methodologies, that is, building artificial systems as a means of exploring these ideas. Two differing approaches can be distinguished: building models of specific animal systems and assessing them within complete behavior-environment loops; and exploring the behavior of invented artificial animals, often called animats, under similar conditions. An obvious question about the latter approach is, how can we learn about real biology from simulation of non-existent animals? In this article I will argue, first, that animat research, to the extent that it is relevant to biology, should also be considered as model building. Animat simulations do, implicitly, represent hypotheses about, and should be evaluated by comparison to, animals. Casting this research in terms of invented agents serves only to limit the ability to draw useful conclusions from it by deflecting or deferring any serious comparisons of the model mechanisms and results with real biological systems. Claims that animat models are meant to be existence proofs, idealizations, or represent general problems in biology do not make these models qualitatively different from more conventional models of specific animals, nor undermine the ultimate requirement to justify this work by making concrete comparisons with empirical data. It is thus suggested that we will learn more by choosing real, and not made-up, targets for our models. [ABSTRACT FROM AUTHOR]

Volition, although often poorly defined, is a concept of interest and utility to both philosophers and researchers in artificial intelligence. In this article, a definition of volition is proposed and a functionally defined, physically grounded ordinal scale and a procedure by which volition might be measured are put forward: a type of Turing test for volition, but motivated by an explicit analysis of the concept being tested and providing results that are graded, rather than Boolean, so that candidate systems may be ranked according to their degree of volitional endowment. It is proposed that volition is a functional, aggregate property of certain physical systems and it is defined as the capacity for adaptive decision-making. The scale, similar in scope to Daniel Dennett's Kinds of Minds scale, is then outlined, as well as a set of progressive "litmus tests" for determining where a candidate system falls on the scale. Such a formulation may be useful for understanding volition and assessing the progress made in engineering intelligent, autonomous artificial organisms. [ABSTRACT FROM AUTHOR]

Volition, although often poorly defined, is a concept of interest and utility to both philosophers and researchers in artificial intelligence. In this article, a definition of volition is proposed and a functionally defined, physically grounded ordinal scale and a procedure by which volition might be measured are put forward: a type of Turing test for volition, but motivated by an explicit analysis of the concept being tested and providing results that are graded, rather than Boolean, so that candidate systems may be ranked according to their degree of volitional endowment. It is proposed that volition is a functional, aggregate property of certain physical systems and it is defined as the capacity for adaptive decisionmaking. The scale, similar in scope to Daniel Dennett's Kinds of Minds scale, is then outlined, as well as a set of progressive "litmus tests" for determining where a candidate system falls on the scale. Such a formulation may be useful for understanding volition and assessing the progress made in engineering intelligent, autonomous artificial organisms. [ABSTRACT FROM AUTHOR]

A number of learning models are commonly employed in the simulation of social behavior. These include population learning, lifetime learning and cultural learning. Population learning allows populations as a whole to evolve over time, typically through a Darwinian model of natural selection. Lifetime learning allows individuals to acquire knowledge during their lifetimes and cultural learning allows individuals to pass this knowledge to their peers or subsequent generations. This work examines the effects of cultural learning on both the fitness and the diversity of a population of neural network agents. A population employing population learning alone and one employing both population and cultural learning are assigned three benchmark tasks: the 5-bit parity problem, the game of tic-tac-toe and the game of connect-four. Each agent contains a genome which encodes a neural network controller used by the agent to perceive and react to environmental stimuli. Results show that the addition of cultural learning promotes improved fitness and significantly increases both genotypic (the genetic make up of individuals) and phenotypic (the behavior of individuals) diversity in the population. [ABSTRACT FROM AUTHOR]

This paper shows how phonological structures can be culturally selected so as to become learnable and adapted to the ecological niche formed by the brains and bodies of speakers. A computational model of the cultural formation of syllable systems illustrates how general learning and physical biases can influence the evolution of the structure of vocalization systems. We use the artificial life methodology of building a society of artificial agents, equipped with motor, perceptual and cognitive systems that are generic and have a realistic complexity. We demonstrate that agents, playing the "imitation game," build shared syllable systems and show how these syllable systems relate to existing human syllable systems. Detailed experiments study the learnability of the self-organized syllable systems. In particular, we reproduce the critical period effect and the artificial language learning effect without the need for innate biases which specify explicitly in advance the form of possible phonological structures. The ability of children agents to learn syllable systems is explained by the cultural evolutionary history of these syllable systems, which were selected for learnability. [ABSTRACT FROM AUTHOR]

The article comments on the perspective of researcher Barbara Webb regarding the potential of the discussions, regarding the importance and novelty of synthesis in artificial life (ALife) and adaptive behavior (AB), to obscure their being fundamentally model-building enterprises. It agrees to the approach of Webb on the ontological mumbo jumbo of the ALife position. It concurs to Webb's tender of rebuttal for those who want to use ALife creations being a source of models.

The author comments on the study by researcher Barbara Webb on the effectiveness of building models of existing animals rather than building ad hoc artificial creature. He agrees on the vitalness of an animat validation and proposes alternative validation criteria to guide comparison with an animal. He opposes the idea regarding the immunity of animats to criticisms of their validity as the only apparent advantage of their use and stresses that animats are of use for biologically relevant study.

A personal narrative is presented which explores the author's experience of meeting robotics engineer Takashi Gomi at the European Conference for Artificial Life (ECAL) in Brussels, Belgium.

The author discusses his interactions with robotic researcher Takashi Gomi during their meetings at conferences in the U.S. and other countries. Topics include Gomi's involvement in a survey conducted by the author for Nippon Telegram and Telephone (NTT), his association with Advanced Telecommunications Research Institute, International (ATR), and the Artificial Life (AL) group. An obituary for Takashi Gomi by the author is presented.

The article discusses the emphasis of the article by researcher Barbara Webb on the relationship between the artificial life and adaptive behavior research programs. It highlights the synthetic point highlighting the significance of the studies of invented artificial animals to be dealt as models of animal behavior. It mentions the importance for the biological models to be based on real biological system in order to maximize the use of animat research for understanding the biology behavior.