Evolução de comportamento adaptivo em robôs por meio da seleção natural

domingo, janeiro 31, 2010

Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection

Dario Floreano1*, Laurent Keller2*

1 Laboratory of Intelligent Systems, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 2 Department of Ecology and Evolution, University of Lausanne, Biophore, Lausanne, Switzerland

Citation: Floreano D, Keller L (2010) Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection. PLoS Biol 8(1): e1000292. doi:10.1371/journal.pbio.1000292

Published: January 26, 2010

Copyright: © 2010 Floreano, Keller. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was partly funded by the Swiss National Science Foundation and by the Future and Emerging Technologies Division of the European Commission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

* E-mail: Dario.Floreano@epfl.ch (DF); Laurent.Keller@unil.ch (LK)

Ever since Cicero's De Natura Deorum ii.34., humans have been intrigued by the origin and mechanisms underlying complexity in nature. Darwin suggested that adaptation and complexity could evolve by natural selection acting successively on numerous small, heritable modifications. But is this enough? Here, we describe selected studies of experimental evolution with robots to illustrate how the process of natural selection can lead to the evolution of complex traits such as adaptive behaviours. Just a few hundred generations of selection are sufficient to allow robots to evolve collision-free movement, homing, sophisticated predator versus prey strategies, coadaptation of brains and bodies, cooperation, and even altruism. In all cases this occurred via selection in robots controlled by a simple neural network, which mutated randomly.

Genes do not specify behaviours directly but rather encode molecular products that lead to the development of brains and bodies through which behaviour is expressed. An important task is therefore to understand how adaptive behaviours can evolve by the mere process of natural selection acting on genes that do not directly code for behaviours. A spectacular demonstration of the power of natural selection comes from experiments in the field of evolutionary robotics [1],[2], where scientists have conducted experimental evolution with robots. Evolutionary robotics has also been advocated as a method to automatically generate control systems that are comparatively simpler or more efficient than those engineered with other design methods because the space of solutions explored by evolution can be larger and less constrained than that explored by conventional engineering methods[3]. In this essay we will examine key experiments that illustrate how, for example, robots whose genes are translated into simple neural networks can evolve the ability to navigate, escape predators, coadapt brains and body morphologies, and cooperate. We present mostly—but not only—experimental results performed in our laboratory, which satisfy the following criteria. First, the experiments were at least partly carried out with real robots, allowing us to present a video showing the behaviours of the evolved robots. Second, the robot's neural networks had a simple architecture with no synaptic plasticity, no ontogenetic development, and no detailed modelling of ion channels and spike transmission. Third, the genomes were directly mapped into the neural network (i.e., no gene-to-gene interaction, time-dependent dynamics, or ontogenetic plasticity). By limiting our analysis to these studies we are able to highlight the strength of the process of Darwinian selection in comparable simple systems exposed to different environmental conditions. There have been numerous other studies of experimental evolution performed with computer simulations of behavioural systems. Reviews of these studies can be found in [4]–[6]. Furthermore, artificial evolution has also been applied to disembodied digital organisms living in computer ecosystems, such as Tierra [7] and Avida[8], to address questions related to gene interactions [9], evolution of complexity [10], and mutation rates [11],[12].