Evolution of Collective Perception in a Group of Autonomous Robots

Morlino, Giuseppe; Trianni, Vito; Tuci, Elio

doi:10.1007/978-3-642-27534-0_5

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…This visual attention model has been further extended and validated on physical autonomous robots in a paper published in this special issue (see Santana and Correia 2011). Morlino et al (2010) describe a simulation model which aims at studying collective perception in a robotic swarm. The goal of this study is to understand which are the selforganising processes underlying the collective perception of a macroscopic environmental feature, which is not accessible to the individual robots due to their limited perceptual abilities and to the nature of their individual exploration strategies.…”

Section: Swarm Cognition and Artificial Lifementioning

confidence: 99%

Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives

Trianni¹,

Tuci

Passino

et al. 2010

Swarm Intell

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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 environment. Progress in disciplines such as neurosciences, cognitive psychology, social ethology and swarm intelligence has allowed researchers to recognise and model the distributed basis of cognition and to draw parallels between the behaviour of social insects and brain dynamics. In this paper, we discuss the theoretical premises and the biological basis of Swarm Cognition, a novel approach to the study of cognition as a distributed self-organising phenomenon, and we point to novel fascinating directions for future work.

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Section: Swarm Cognition and Artificial Lifementioning

confidence: 99%

Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives

Trianni¹,

Tuci

Passino

et al. 2010

Swarm Intell

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“…Evolutionary Robotics, like the origin of learning study mentioned above, is full of experiments that can be viewed as comparative (see also [5], [15] and [17]). Similarly, Ponticorvo and Miglino's work [4] compares a simulation with a variety of natural behaviours.…”

Section: Comparative Modeling and Engineering Approachesmentioning

confidence: 99%

Natural and Artificial Systems: Compare, Model or Engineer?

Vassie¹,

Morlino

2012

From Animals to Animats 12

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Some areas of biological research use artificial means to explore the natural world. But how the natural and artificial are related across wideranging research areas is not always clear. Relations differ further for bioengineering fields. We propose a taxonomy which would serve to elucidate distinct relations; there are three ways in which the natural is linked to the artificial, corresponding with distinct methods of investigation: i) a comparative approach (natural vs artificial) in which artificial systems are treated in the same way as natural systems, ii) a modeling approach (natural via artificial) in which we use artificial systems to learn about features of natural ones, and iii) an engineering approach (natural pro artificial) in which natural systems are used to draw inspiration for artefacts. Ambiguities about and between these approaches limit the development of fields and impact negatively on interdisciplinary communication.

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