Evolution of Signaling in a Multi-Robot System: Categorization and Communication

  title={Evolution of Signaling in a Multi-Robot System: Categorization and Communication},
  author={Christos Ampatzis and Elio Tuci and V. Trianni and Marco Dorigo},
  journal={Adaptive Behavior},
  pages={26 - 5}
Communication is of central importance in collective robotics, as it is integral to the switch from solitary to social behavior. In this article, we study emergent communication behaviors that are not predetermined by the experimenter, but are shaped by artificial evolution, together with the rest of the behavioral repertoire of the robots. In particular, we describe a set of experiments in which artificial evolution is used as a means to engineer robot neuro-controllers capable of guiding… 

Figures and Tables from this paper

The evolution of communication in robot societies

These findings explain why communicative strategies are so variable in many animal species when interests between them conflict and predict that relatedness will play an important role in the evolution of signal reliability in natural systems of communication.

Language Evolution in Swarm Robotics: A Perspective

It is suggested that swarm robotics can be an ideal test-bed to advance research on the emergence of language-like communication, and the latter can be key to provide robot swarms with additional skills to support self-organization and adaptivity, enabling the design of more complex collective behaviors.

An investigation of the evolutionary origin of reciprocal communication using simulated autonomous agents

  • Elio Tuci
  • Biology, Psychology
    Biological Cybernetics
  • 2009
The phylogeny of successful communication protocol is looked into, and the evolutionary phenomena that, in early evolutionary stages, paved the way for the subsequent development of reciprocal communication, categorisation capabilities and successful cooperative strategies are described.

To Grip, or Not to Grip: Evolving Coordination in Autonomous Robots

It is shown how robot coordination and individual choices can be successfully restated in terms of anti-coordination problems, showing how conventional game theoretical tools can be used in the interpretation and design of evolutionary outcomes in collective robotics.

Evolving Homogeneous Neurocontrollers for a Group of Heterogeneous Robots: Coordinated Motion, Cooperation, and Acoustic Communication

The results of this study show that dynamic artificial neural networks can be successfully synthesized by artificial evolution to design the neural mechanisms required to underpin the behavioral strategies and adaptive communication capabilities demanded by this task.

Engineering the Evolution of Self-Organizing Behaviors in Swarm Robotics: A Case Study

It is shown that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size, and also presents a case study about self-organizing synchronization in a swarm of robots.

GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics

This paper proposes GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics, and develops a grammar that can generate a rich variety of collective behaviors.

Artificial Life 13

The extended model predicts that the addition of local communication, in conjunction with the topology of the group, results in higher expected success in attempting collective movements for individuals with central locations in the group as compared to individuals occupying edge locations.

Using Communication for the Evolution of Scalable Role Allocation in Collective Robotics

This paper shows that rewarding signal differentiation is not necessary to evolve communication-based role allocation strategies for the given task, and it improves reported scalability, while requiring less a priori knowledge.

Modular automatic design of collective behaviors for robots endowed with local communication capabilities

This article introduces Gianduja an automatic design method that generates collective behaviors for robot swarms in which individuals can locally exchange a message whose semantics is not a priori fixed, and compares it with a standard neuro-evolutionary approach.



Evolutionary Conditions for the Emergence of Communication in Robots

Self-organisation and communication in groups of simulated and physical robots

This work presents an experimental study of self-organising behaviours for a group of robots, which exploit communication to coordinate their activities, suggesting that artificial evolution can produce behaviours that are more adaptive than those obtained with conventional design methodologies.

Feeling the Flow of Time trough Sensory-Motor Coordination

The results of this work show that a single dynamic neural network, shaped by evolution, makes an autonomous agent capable of “feeling” time through the flow of sensations determined by its actions.

How Producer Biases Can Favor the Evolution of Communication: An Analysis of Evolutionary Dynamics

This article explores the problem of co-evolution of speakers and hearers with artificial life simulations: a population of artificial neural networks evolving a food call system and reveals an important factor, which might solve the phylogenetic problem.

Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines

This book describes the basic concepts and methodologies of evolutionary robotics and the results achieved so far, and describes the clear presentation of a set of empirical experiments of increasing complexity.

How Hierarchical Control Self-organizes in Artificial Adaptive Systems

A hierarchical neural network is shown to outperform a comparable single-level network in controlling a mobile robot and to improve system performance by decreasing interference between different parts of the network.

‘Feeling’ the flow of time through sensorimotor co-ordination

The results of this work show that a single dynamic neural network, shaped by evolution, makes an autonomous agent capable of ‘feeling’ time through the flow of sensations determined by its actions.

Evolving Dynamical Neural Networks for Adaptive Behavior

It is demonstrated that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers.

Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments

The results show that evolutionary adaptive controllers solve the task much faster and better than evolutionary standard fixed-weight controllers, that the method scales up well to large architectures, and that they can adapt to environmental changes that involve new sensory characteristics and new spatial relationships.

Cooperation through self-assembly in multi-robot systems

The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly.