Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
Dario Floreano,Francesco Mondada +1 more
- pp 421-430
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TLDR
The paper describes the results of the evolutionary development of a real, neural-network driven mobile robot, and shows a number of emergent phenomena that are characteristic of autonomous agents.Abstract:
The paper describes the results of the evolutionary development of a real, neural-network driven mobile robot. The evolutionary approach to the development of neural controllers for autonomous agents has been successfully used by many researchers, but most - if not all - studies have been carried out with computer simulations. Instead, in this research the whole evolutionary process takes places entirely on a real robot without human intervention. Although the experiments described here tackle a simple task of navigation and obstacle avoidance, we show a number of emergent phenomena that are characteristic of autonomous agents. The neural controllers of the evolved best individuals display a full exploitation of non-linear and recurrent connections that make them more efficient than analogous man-designed agents. In order to fully understand and describe the robot behavior, we have also employed quantitative ethological tools [13], and showed that the adaptation dynamics conform to predictions made for animals.read more
Citations
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Book ChapterDOI
A Case Study on the Scalability of Online Evolution of Robotic Controllers
TL;DR: It is shown how online evolution algorithms can enable groups of different size to leverage their multiplicity, and how larger groups can achieve superior task performance and enable a significant reduction in the evolution time and in the number of evaluations required to evolve controllers that solve the task.
Evolving Robocode Tank Fighters
TL;DR: The application of genetic programming to evolve a controller for a robotic tank in a simulated environment using a fixed subsumption architecture of TableRex modules, resulting in robots that beat some of the most competitive hand-coded adversaries.
Book ChapterDOI
Evolving creatures in virtual ecosystems
TL;DR: The results of evolving creatures being able to crawl, to walk and to climb stairs are shown.
Proceedings ArticleDOI
A Neural Network-based kinematic and light-perception simulator for simple robotic evolution
TL;DR: ANNs were employed to simulate the motion dynamics of a mobile robot steered using differential steering, as well as the interaction of two light sensors onboard the robot with a light source in its vicinity, indicating that ANNs show definite promise as robot simulators.
Journal ArticleDOI
Evolving robot sub-behaviour modules using Gene Expression Programming
Jonathan Mwaura,Edward Keedwell +1 more
TL;DR: The paper introduces Regulatory Multigenic Gene Expression Programming, a new evolutionary technique that can be utilised to automatically evolve modularity in robot behaviour.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Intelligence without Representation
TL;DR: Brooks et al. as mentioned in this paper decompose an intelligent system into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much.
Journal ArticleDOI
Intelligence without representation
TL;DR: Brooks et al. as discussed by the authors decompose an intelligent system into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much.