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Innocent Until Proven Guilty: Reducing Robot Shaping from Polynomial to Linear Time

IEEE TRANSACTIONS ON evolutionary COMPUTATION 2011年第4期15卷 571-585页

作者： Bongard, Josh C. Univ Vermont Dept Comp Sci Burlington VT 05405 USA

In evolutionary algorithms, much time is spent evaluating inferior phenotypes that produce no offspring. A common heuristic to address this inefficiency is to stop evaluations early if they hold little promise of attaining high fitness. However, the form of this heuristic is typically dependent on the fitness function used, and there is a danger of prematurely stopping evaluation of a phenotype that may have recovered in the remainder of the evaluation period. Here a stopping method is introduced that gradually reduces fitness over the phenotype's evaluation, rather than accumulating fitness. This method is independent of the fitness function used, only stops those phenotypes that are guaranteed to become inferior to the current offspring-producing phenotypes, and realizes significant time savings across several evolutionary robotics tasks. It was found that for many tasks, time complexity was reduced from polynomial to sublinear time, and time savings increased with the number of training instances used to evaluate a phenotype as well as with task difficulty.

关键词： Early stopping evolutionary robotics

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Task allocation for robots using inspiration from hormones

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ADAPTIVE BEHAVIOR 2011年第3期19卷 208-224页

作者： Walker, Joanne H. Wilson, Myra S. Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

This article describes a robotic system which uses evolution to continuously adapt a group of heterogeneous robots to their current environment while assigning tasks to these robots using an endocrine-based system. The tasks are allocated dependent on the robots' current ability to perform the task and whether the task is being done by another robot. A series of experiments is presented taking the work from an evolutionary training phase, through simulation trials, to experiments on real robots. The real robot trials show task swapping dependent on the robots' ability to perform each task.

关键词： evolutionary robotics endocrine system task distribution

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Morphological change in machines accelerates the evolution of robust behavior

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2011年第4期108卷 1234-1239页

作者： Bongard, Josh Univ Vermont Burlington VT 05405 USA

Most animals exhibit significant neurological and morphological change throughout their lifetime. No robots to date, however, grow new morphological structure while behaving. This is due to technological limitations but also because it is unclear that morphological change provides a benefit to the acquisition of robust behavior in machines. Here I show that in evolving populations of simulated robots, if robots grow from anguilliform into legged robots during their lifetime in the early stages of evolution, and the anguilliform body plan is gradually lost during later stages of evolution, gaits are evolved for the final, legged form of the robot more rapidly-and the evolved gaits are more robust-compared to evolving populations of legged robots that do not transition through the anguilliform body plan. This suggests that morphological change, as well as the evolution of development, are two important processes that improve the automatic generation of robust behaviors for machines. It also provides an experimental platform for investigating the relationship between the evolution of development and robust behavior in biological organisms.

关键词： evolutionary computation robotics evolutionary robotics biomechanics locomotion

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Behavioural robustness: A link between distributed mechanisms and coupled transient dynamics

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BIOSYSTEMS 2011年第1期105卷 49-61页

作者： Fernandez-Leon, Jose A. Univ Sussex CCNR Brighton BN1 9QG E Sussex England

The emergence of a unified cognitive behaviour relies on the coordination of specialized components that distribute across a 'brain', body and environment. Although a general dynamical mechanism involved in agent-environment integration is still largely unknown for behavioural robustness, discussions here are focussed on one of the most plausible candidate: the formation of distributed mechanisms working in transient during agent-environment coupling. This article provides discussions on this sort of coordination based on a mobile object-tracking task with situated, embodied and minimal agents, and tests for robust yet adaptive behaviour. The proposed scenario provides examples of behavioural mechanisms that counterbalance the functional organization of internal control activity and agents' situatedness to enable the evolution of a two-agent interaction task. Discussions in this article suggest that future studies of distributed cognition should take into account that there are at least two possible modes of interpreting distributed mechanisms and that these have a qualitatively different effect on behavioural robustness. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

关键词： Behavioural robustness Animat evolutionary robotics Genetic algorithm Two-agent interaction Transient dynamics

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Darwinian embodied evolution of the learning ability for survival

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ADAPTIVE BEHAVIOR 2011年第2期19卷 101-120页

作者： Elfwing, Stefan Uchibe, Eiji Doya, Kenji Christensen, Henrik I. JST Okinawa Inst Sci & Technol Initial Res Project Neural Computat Unit Okinawa 9042234 Japan Royal Inst Technol KTH Ctr Autonomous Syst Numer Anal & Comp Sci Stockholm Sweden

In this article we propose a framework for performing embodied evolution with a limited number of robots, by utilizing time-sharing in subpopulations of virtual agents hosted in each robot. Within this framework, we explore the combination of within-generation learning of basic survival behaviors by reinforcement learning, and evolutionary adaptations over the generations of the basic behavior selection policy, the reward functions, and metaparameters for reinforcement learning. We apply a biologically inspired selection scheme, in which there is no explicit communication of the individuals' fitness information. The individuals can only reproduce offspring by mating-a pair-wise exchange of genotypes-and the probability that an individual reproduces offspring in its own subpopulation is dependent on the individual's "health," that is, energy level, at the mating occasion. We validate the proposed method by comparing it with evolution using standard centralized selection, in simulation, and by transferring the obtained solutions to hardware using two real robots.

关键词： Embodied evolution evolutionary robotics reinforcement learning meta-learning shaping rewards metaparameters

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Flexible and multistable pattern generation by evolving constrained plastic neurocontrollers

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ADAPTIVE BEHAVIOR 2011年第3期19卷 187-207页

作者： Hoinville, Thierry Siles, Cecilia Tapia Henaff, Patrick Italian Inst Technol I-16163 Genoa Italy Univ Cergy Pontoise ETIS ETIS Lab CNRSUMR 8051 Cergy Pontoise France

In evolutionary robotics, plastic neural network models proved to be promising for evolving adaptive behaviors. In particular, neurocontrollers incorporating hebbian synapses have been shown to be useful for implementing conflicting sub-behaviors. Numerous interesting complex tasks assume such flexibility. However, those evolved controllers often exhibit behavioral instability, as simulation time is extended beyond the short limit used during evolution. In this paper, we propose constrained plastic models inspired by neural homeostasis phenomena, in order to evolve flexible and stable pattern generators for single-legged locomotion. Comparative results show that constrained controllers perform better than unconstrained ones in both terms of evolvability and behavioral stability. Functional analyses of the best evolved controller unveil the adaptivity, robustness and homeostasis arising from the statically constrained plasticity. Interestingly, homeostasis evolved implicitly without relying on any active homeostatic mechanisms and is implemented through hebbian plasticity, usually considered destabilizing.

关键词： Homeostasis plastic neural networks adaptive synapses legged locomotion evolutionary robotics

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Evolution of a predictive internal model in an embodied and situated agent

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THEORY IN BIOSCIENCES 2011年第4期130卷 259-276页

作者： Gigliotta, Onofrio Pezzulo, Giovanni Nolfi, Sefano Univ Naples Federico II Nat & Artificial Cognit Lab I-80133 Naples Italy Ist Linguist Computazionale Antonio Zampolli I-56124 Pisa Italy CNR Inst Cognit Sci & Technol I-00185 Rome Italy

We show how simulated robots evolved for the ability to display a context-dependent periodic behavior can spontaneously develop an internal model and rely on it to fulfill their task when sensory stimulation is temporarily unavailable. The analysis of some of the best evolved agents indicates that their internal model operates by anticipating sensory stimuli. More precisely, it anticipates functional properties of the next sensory state rather than the exact state that sensors will assume. The characteristics of the states that are anticipated and of the sensorimotor rules that determine how the agents react to the experienced states, however, ensure that they produce very similar behaviour during normal and blind phases in which sensory stimulation is available or is self-generated by the agent, respectively. Agents' internal models also ensure an effective transition during the phases in which agents' internal dynamics is decoupled and re-coupled with the sensorimotor flow. Our results suggest that internal models might have arisen for behavioral reasons and successively exapted for other cognitive functions. Moreover, the obtained results suggest that self-generated internal states should not necessarily match in detail the corresponding sensory states and might rather encode more abstract and motor-oriented information.

关键词： Internal models evolutionary robotics Prediction

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Evolving experience-dependent robust behaviour in embodied agents

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BIOSYSTEMS 2011年第1期103卷 45-56页

作者： Fernandez-Leon, Jose A. Univ Sussex CCNR Brighton BN1 9QG E Sussex England

In this work, based on behavioural and dynamical evidence, a study of simulated agents with the capacity to change feedback from their bodies to accomplish a one-legged walking task is proposed to understand the emergence of coupled dynamics for robust behaviour. Agents evolve with evolutionary-defined biases that modify incoming body signals (sensory offsets). Analyses on whether these agents show further dependence to their environmental coupled dynamics than others with no feedback control is described in this article. The ability to sustain behaviours is tested during lifetime experiments with mutational and sensory perturbations after evolution. Using dynamical systems analysis, this work identifies conditions for the emergence of dynamical mechanisms that remain functional despite sensory perturbations. Results indicate that evolved agents with evolvable sensory offset depends not only on where in neural space the state of the neural system operates, but also on the transients to which the inner-system was being driven by sensory signals from its interactions with the environment, controller, and agent body. Experimental evidence here leads discussions on a dynamical systems perspective on behavioural robustness that goes beyond attractors of controller phase space. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

关键词： Behavioural robustness Animat evolutionary robotics Genetic algorithm Walking behaviour Transient dynamics

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Combining simulation and reality in evolutionary robotics

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2007年第1期50卷 19-39页

作者： Zagal, Juan Cristobal Ruiz-del-Solar, Javier Univ Chile Dept Elect Engn Santiago 6513027 Chile

evolutionary robotics (ER) is a promising methodology, intended for the autonomous development of robots, in which their behaviors are obtained as a consequence of the structural coupling between robot and environment. It is essential that there be a great amount of interaction to generate complex behaviors. Thus, nowadays, it is common to use simulation to speed up the learning process;however simulations are achieved from arbitrary off-line designs, rather than from the result of embodied cognitive processes. According to the reality gap problem, controllers evolved in simulation usually do not allow the same behavior to arise once transferred to the real robot. Some preliminary approaches for combining simulation and reality exist in the ER literature;nonetheless, there is no satisfactory solution available. In this work we discuss recent advances in neuroscience as a motivation for the use of environmentally adapted simulations, which can be achieved through the co-evolution of robot behavior and simulator. We present an algorithm in which only the differences between the behavior fitness obtained in reality versus that obtained in simulations are used as feedback for adapting a simulation. The proposed algorithm is experimentally validated by showing the successful development and continuous transference to reality of two complex low-level behaviors with Sony AIBO1 robots: gait optimization and ball-kicking behavior.

关键词： AIBO robots evolutionary robotics mobile robotics organismically inspired robotics RoboCup simulation of solid dynamics

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The Effects of Using a Greedy Factor in Hexapod Gait Learning

The Effects of Using a Greedy Factor in Hexapod Gait Learnin...

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IEEE Congress on evolutionary Computation (CEC)

作者： Parker, Gary B. Tarimo, William T. Connecticut Coll Dept Comp Sci New London CT 06320 USA

ISBN: (纸本)9781424478354

Various selection schemes have been described for use in genetic algorithms. This paper investigates the effects of adding greediness to the standard roulette-wheel selection. The results of this study are tested on a Cyclic Genetic Algorithm (CGA) used for learning gaits for a hexapod servo-robot. The effectiveness of CGA in learning optimal gaits with selection based on roulette-wheel selection with and without greediness is compared. The results were analyzed based on fitness of the individual gaits, convergence time of the evolution process, and the fitness of the entire population evolved. Results demonstrate that selection with too much greediness tends to prematurely converge with a sub-optimal solution, which results in poorer performance compared to the standard roulette-wheel selection. On the other hand, roulette-wheel selection with very low greediness evolves more diverse and fitter populations with individuals that result in the desired optimal gaits.

关键词： Genetic Algorithm Cyclic Control Hexapod Greedy Selection Gait evolutionary robotics Learning Control Cyclic Genetic Algorithm

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