On the Design of Generalist Strategies for Swarms of Simulated Robots Engaged in Task-allocation Scenarios

This study focuses on issues related to the evolutionary design of task-allocation mechanisms for swarm robotics systems with agents potentially capable of performing different tasks. Task allocation in swarm robotics refers to a process that results in the distribution of robots to different concurrent tasks without any central or hierarchical control. In this paper, we investigate a scenario with two concurrent tasks (i.e. foraging and nest patrolling) and two environments in which the task priorities vary. We are interested in generating successful groups made of behaviourally plastic agents (i.e. agents that are capable of carrying out different tasks in different environmental conditions), which could adapt their task preferences to those of their group mates as well as to the environmental conditions. We compare the results of three different evolutionary design approaches, which differ in terms of the agents’ genetic relatedness (i.e. groups of clones and groups of unrelated individuals), and/or the selection criteria used to create new populations (i.e. single and multi-objective evolutionary optimisation algorithms). We show results indicating that the evolutionary approach based on the use of genetically unrelated individuals in combination with a multi-objective evolutionary optimisation algorithm has a better success rate then an evolutionary approach based on the use of genetically related agents. Moreover, the multi-objective approach, when compared to a single-objective approach and genetically unrelated individual, significantly limits the tendency towards task specialisation by favouring the emergence of generalist agents without introducing extra computational costs. The significance of this result is discussed in view of the relationship between individual behavioural skills and swarm effectiveness

Swarm Cognition and Artificial Life

Abstract. Swarm Cognition is the juxtaposition of two relatively un-related concepts that evoke, on the one hand, the power of collective behaviours displayed by natural swarms, and on the other hand the com-plexity of cognitive processes in the vertebrate brain. Recently, scientists from various disciplines suggest that, at a certain level of description, op-erational principles used to account for the behaviour of natural swarms may turn out to be extremely powerful tools to identify the neuroscien-tific basis of cognition. In this paper, we review the most recent studies in this direction, and propose an integration of Swarm Cognition with Artificial Life, identifying a roadmap for a scientific and technological breakthrough in Cognitive Sciences.

From symbol grounding to socially shared embodied language knowledge

Much language-related research in cognitive robotics appeals to usage-based models of language as proposed in cognitive linguistics and developmental psychology [1, 2] that emphasise the significance of learning, embodiment and general cognitive development for human language acquisition. Over and above these issues, however, what takes centre stage in these theories are social-cognitive skills of “intention-reading” that are seen as “primary in the language acquisition process” [1] – and also as difficult to incorporate into computational models of language acquisition. The present paper addresses these concerns: we describe work in progress on a series of experiments that take steps towards closing the gap between ‘solipsistic’ symbol grounding in individual robotic agents and socially framed embodied language acquisition in learners that attend to common ground [3] with changing interlocutors

Active categorical perception in an evolved anthropomorphic robotic arm

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 cognitive 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 between perceptual categories and the body-environment interactions can be experimentally manipulated. In this pa-per, we study the mechanisms of tactile perception in a task in which a neuro-controlled anthropomorphic robotic arm, equipped with coarse-grained tactile sen-sors, is required to perceptually discriminate between spherical and ellipsoid ob-jects. The results of this work demonstrate that evolved continuous time non-linear neural controllers can bring forth strategies to allow the arm to effectively solve the discrimination task.

Evolution of Neuro-Controllers for Robots' Alignment using Local Communication

info:eu-repo/semantics/publishe

Evolution of Neuro-Controllers for Robots\u27 Alignment using Local Communication

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 require local communications that are physically implemented using infrared signalling. We study the performance of the evolved controllers, both in simulation and in reality for different group sizes. In addition, we analyze the most successful communication strategy developed using artificial evolution

Evolutionary Robotics: a new scientific tool for studying cognition

We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics 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 systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment