Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics

Roborobo! is a multi-platform, highly portable, robot simulator for large-scale collective robotics experiments. Roborobo! is coded in C++, and follows the KISS guideline ("Keep it simple"). Therefore, its external dependency is solely limited to the widely available SDL library for fast 2D Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast single and multi-robots simulation, and has already been used in more than a dozen published research mainly concerned with evolutionary swarm robotics, including environment-driven self-adaptation and distributed evolutionary optimization, as well as online onboard embodied evolution and embodied morphogenesis.Comment: 2 pages, 1 figur

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Grounding Artificial Intelligence in the Origins of Human Behavior

Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. However, although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes that may have guided the emergence of complex cognitive capacities during the evolution of the species. Research in Human Behavioral Ecology (HBE) seeks to understand how the behaviors characterizing human nature can be conceived as adaptive responses to major changes in the structure of our ecological niche. In this paper, we propose a framework highlighting the role of environmental complexity in open-ended skill acquisition, grounded in major hypotheses from HBE and recent contributions in Reinforcement learning (RL). We use this framework to highlight fundamental links between the two disciplines, as well as to identify feedback loops that bootstrap ecological complexity and create promising research directions for AI researchers

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi

Growing through Sabotage: Energizing Hierarchical Power

casp energy growth hierarchy power sabotageAccording to the theory of capital as power, capitalism, like any other mode of power, is born through sabotage and lives in chains – and yet everywhere we look we see it grow and expand. What explains this apparent puzzle of 'growth in the midst of sabotage'? The answer, we argue, begins with the very meaning of ‘growth’. Whereas conventional political economy equates the growth with a rising standard of living, we posit that much of this growth has nothing to do with livelihood as such: it represents not the improvement of wellbeing, but the expansion of sabotage itself. Building on this premise, the article historicizes, theorizes and models the relationship between changes in hierarchical power and sabotage on the one hand and the growth of energy capture on the other. It claims that hierarchical power is sought for its own sake; that building and sustaining this power demands strategic sabotage; and that sabotage absorbs a significant proportion of the energy captured by society. From this standpoint, capitalism grows, at least in part, not despite or because of sabotage, but through sabotage

Growing through Sabotage: Energizing Hierarchical Power

According to the theory of capital as power, capitalism, like any other mode of power, is born through sabotage and lives in chains – and yet everywhere we look we see it grow and expand. What explains this apparent puzzle of ‘growth in the midst of sabotage’? The answer, we argue, begins with the very meaning of ‘growth’. Whereas conventional political economy equates growth with a rising standard of living, we posit that much of this growth has nothing to do with livelihood as such: it represents not the improvement of wellbeing, but the expansion of sabotage itself. Building on this premise, the article historicizes, theorizes and models the relationship between changes in hierarchical power and sabotage on the one hand and the growth of energy capture on the other. It claims that hierarchical power is sought for its own sake; that building and sustaining this power demands strategic sabotage; and that sabotage absorbs a significant proportion of the energy captured by society. From this standpoint, capitalism grows, at least in part, not despite but because of – and indeed through – sabotage

Discovering Communication

What kind of motivation drives child language development? This article presents a computational model and a robotic experiment to articulate the hypothesis that children discover communication as a result of exploring and playing with their environment. The considered robotic agent is intrinsically motivated towards situations in which it optimally progresses in learning. To experience optimal learning progress, it must avoid situations already familiar but also situations where nothing can be learnt. The robot is placed in an environment in which both communicating and non-communicating objects are present. As a consequence of its intrinsic motivation, the robot explores this environment in an organized manner focusing first on non-communicative activities and then discovering the learning potential of certain types of interactive behaviour. In this experiment, the agent ends up being interested by communication through vocal interactions without having a specific drive for communication

Sociobiology, universal Darwinism and their transcendence: An investigation of the history, philosophy and critique of Darwinian paradigms, especially gene-Darwinism, process-Darwinism, and their types of reductionism towards a theory of the evolution of evolutionary processes, evolutionary freedom and ecological idealism

Based on a review of different Darwinian paradigms, particularly sociobiology, this work, both, historically and philosophically, develops a metaphysic of gene-Darwinism and process-Darwinism, and then criticises and transcends these Darwinian paradigms in order to achieve a truly evolutionary theory of evolution. Part I introduces essential aspects of current sociobiology as the original challenge to this investigation. The claim of some sociobiologists that ethics should become biologized in a gene-egoistic way, is shown to be tied to certain biological views, which ethically lead to problematic results. In part II a historical investigation into sociobiology and Darwinism in general provides us, as historical epistemology', with a deeper understanding of the structure and background of these approaches. Gene-Darwinism, which presently dominates sociobiology and is linked to Dawkins' selfish gene view of evolution, is compared to Darwin's Darwinism and the evolutionary' synthesis and becomes defined more strictly. An account of the external history of Darwinism and its subparadigms shows how cultural intellectual presuppositions, like Malthusianism or the Newtonian concept of the unchangeable laws of nature, also influenced biological theory' construction. In part III universal 'process-Darwinism' is elaborated based on the historical interaction of Darwinism with non-biological subject areas. Building blocks for this are found in psychology, the theory of science and economics. Additionally, a metaphysical argument for the universality of process- Darwinism, linked to Hume's and Popper's problem of induction, is proposed. In part IV gene-Darwinism and process-Darwinism are criticised. Gene-Darwinism—despite its merits—is challenged as being one-sided in advocating 'gene-atomism', 'germ-line reductionism' and 'process-monism'. My alternative proposals develop and try to unify different criticisms often found. In respect of gene-atomism I advocate a many-level approach, opposing the necessary radical selfishness of single genes. I develop the concept of higher-level genes, propose a concept of systemic selection, which may stabilise group properties, without relying on permanent group selection and extend the applicability of a certain group selectionist model generally to small open groups. Proposals of mine linked to the critique of germ-line reductionism are: 'exformation', phenotypes as evolutionary factors and a field theoretic understanding of causa formalis (resembling Aristotelian hylemorphism). Finally the process-monism of gene-Darwinism, process-Darwinism and, if defined strictly, Darwinism in general is criticised. 1 argue that our ontology and ethics would be improved by replacing the Newtoman-Paleyian deist metaphor of an eternal and unchangeable law of nature, which lies at tire very heart of Darwinism, by a truly evolutionary understanding of evolution where new processes may gain a certain autonomy. All this results in a view that I call 'ecological idealism', which, although still very much based on Darwinism, clearly transcends a Darwinian world view

Neural dynamics of social behavior : An evolutionary and mechanistic perspective on communication, cooperation, and competition among situated agents

Social behavior can be found on almost every level of life, ranging from microorganisms to human societies. However, explaining the evolutionary emergence of cooperation, communication, or competition still challenges modern biology. The most common approaches to this problem are based on game-theoretic models. The problem is that these models often assume fixed and limited rules and actions that individual agents can choose from, which excludes the dynamical nature of the mechanisms that underlie the behavior of living systems. So far, there exists a lack of convincing modeling approaches to investigate the emergence of social behavior from a mechanistic and evolutionary perspective. Instead of studying animals, the methodology employed in this thesis combines several aspects from alternative approaches to study behavior in a rather novel way. Robotic models are considered as individual agents which are controlled by recurrent neural networks representing non-linear dynamical system. The topology and parameters of these networks are evolved following an open-ended evolution approach, that is, individuals are not evaluated on high-level goals or optimized for specific functions. Instead, agents compete for limited resources to enhance their chance of survival. Further, there is no restriction with respect to how individuals interact with their environment or with each other. As its main objective, this thesis aims at a complementary approach for studying not only the evolution, but also the mechanisms of basic forms of communication. For this purpose it can be shown that a robot does not necessarily have to be as complex as a human, not even as complex as a bacterium. The strength of this approach is that it deals with rather simple, yet complete and situated systems, facing similar real world problems as animals do, such as sensory noise or dynamically changing environments. The experimental part of this thesis is substantiated in a five-part examination. First, self-organized aggregation patterns are discussed. Second, the advantages of evolving decentralized control with respect to behavioral robustness and flexibility is demonstrated. Third, it is shown that only minimalistic local acoustic communication is required to coordinate the behavior of large groups. This is followed by investigations of the evolutionary emergence of communication. Finally, it is shown how already evolved communicative behavior changes during further evolution when a population is confronted with competition about limited environmental resources. All presented experiments entail thorough analysis of the dynamical mechanisms that underlie evolved communication systems, which has not been done so far in the context of cooperative behavior. This framework leads to a better understanding of the relation between intrinsic neurodynamics and observable agent-environment interactions. The results discussed here provide a new perspective on the evolution of cooperation because they deal with aspects largely neglected in traditional approaches, aspects such as embodiment, situatedness, and the dynamical nature of the mechanisms that underlie behavior. For the first time, it can be demonstrated how noise influences specific signaling strategies and that versatile dynamics of very small-scale neural networks embedded in sensory-motor feedback loops give rise to sophisticated forms of communication such as signal coordination, cooperative intraspecific communication, and, most intriguingly, aggressive interspecific signaling. Further, the results demonstrate the development of counteractive niche construction based on a modification of communication strategies which generates an evolutionary feedback resulting in an active reduction of selection pressure, which has not been shown so far. Thus, the novel findings presented here strongly support the complementary nature of robotic experiments to study the evolution and mechanisms of communication and cooperation.</p