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We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor(More)
—Active perception refers to a theoretical approach to the study of perception grounded on the idea that perceiving is a way of acting, rather than a process whereby the brain constructs an internal representation of the world. The operational principles of active perception can be effectively tested by building robot-based models in which the relationship(More)
— This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously,(More)
This paper provides an overview of the SWARM-BOTS project, a robotic project sponsored by the Future and Emerging Technologies program of the European Commission. The paper illustrates the goals of the project, the robot prototype and the 3D simulator we built. It also reports on the results of experimental work in which distributed adaptive controllers are(More)
We are interested in the construction of ecological models of the evolution of learning behavior using methodological tools developed in the field of evolutionary robotics. In this article, we explore the applicability of integrated (i.e., nonmodular) neural networks with fixed connection weights and simple " leaky-integrator " neurons as controllers for(More)
Basic elements of cognition have been identified in the behaviour displayed by animal collectives, ranging from honeybee swarms to human societies. For example, an insect swarm is often considered a " super-organism " that appears to exhibit cognitive behaviour as a result of the interactions among the individual insects and between the insects and the(More)
—Populations of simulated agents controlled by dy-namical neural networks are trained by artificial evolution to access linguistic instructions and to execute them by indicating, touching or moving specific target objects. During training the agent experiences only a subset of all object/action pairs. During post-evaluation, some of the successful agents(More)
This study compares two different evolutionary approaches to the design of homogeneous multi-robot teams in a task that requires the agents to specialise in different roles. Our results diverge from what illustrated in a previous similar comparative study, which advocates for the superiority of the aclonal versus the clonal approach. We question this(More)
In this paper, we use artificial evolution to design homogeneous neural network controller for groups of robots required to align. Aligning refers to the process by which the robots managed to head towards a common arbitrary and autonomously chosen direction starting from initial randomly chosen orientations. The cooperative interactions among robots(More)