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2010 IEEE World Congress on Computational Intelligence

作者： Ohkura, Kazuhiro Yasuda, Toshiyuki Matsumura, Yoshiyuki Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima 7398527 Japan Shinshu Univ Fac Text Sci & Technol Nagano 386 8567 Japan

ISBN: (纸本)9781424481262

Swarm robotics (SR) is the research field of multi-robot systems, which consist of many homogeneous autonomous robots without any types of global controllers. Generally, since a task given to this system cannot be achieved by a single robot, cooperative behavior is expected to emerge in a robotic swarm by a certain mechanism, which is through the interactions among robots or with an environment. In this paper, an evolutionary robotics approach, in which robot controllers are designed by evolving artificial neural networks, is adopted. Among the many approaches to evolving artificial neural networks, two approaches, NEAT and MBEANN are adopted for conducting computer simulations. Although a conventional neural network has a fixed topology and evolves only with its synaptic weights, NEAT and MBEANN evolve not only with their synaptic weights, but also with their topologies. As a benchmark for swarm robotics, cooperative package-pushing problems using ten autonomous robots are conducted to evaluate their performance. The behavioral characteristics that emerge are then discussed.

关键词： adaptive behavior coordination adaptive systems control system synthesis cooperative behavior cooperative package-pushing problem evolutionary computation evolutionary robotics homogeneous autonomous robots multi-robot systems multirobot system neurocontrollers robot controller design robot interaction swarm robotic system synaptic weight topology topology weight evolving artificial neural network

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A duration based behavior analyze approach for swarm robotics system 54

A duration based behavior analyze approach for swarm robotic...

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2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

作者： Yu, Tian Yasuda, Toshiyuki Ohkura, Kazuhiro Hiroshima Univ Grad Sch Engn Hiroshima 730 Japan

ISBN: (纸本)9784907764487

Swarm robotics is a new approach to the coordination of large numbers of homogeneous robots that takes inspiration from social insects. One of the major topics of SRS that have been getting attention from the early stage is the design methodology of robot controllers. On the other hand, no general methods for analyzing its collective behavior have been developed so far. In this paper, we propose a method based on the duration of subgroup's behavioral sequence for analyzing task allocation in subgroups of a swarm robotics system. The proposed method shows that SRS takes most of the time searching for food sources and the behavior sequences duration over 100 time steps increased as the result of artificial evolution, which helps us understand the role changing of subgroups in the food foraging problem from the view point of duration time.

关键词： swarm robotics system evolutionary robotics duration time complex networks

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The grounding of motivation in artificial animals: Indices of motivational behavior

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Cognitive Systems Research 2003年第1期4卷 23-55页

作者： Savage, Tony School of Psychology The Queen's University of Belfast Belfast BT7 1NN United Kingdom

The grounding issue in AI research is concerned with how best to integrate an artificial creature, such as a robot, with the environment in which it functions. In many investigations these interactions are defined and imposed on the agent by an external source, e.g. the designer of the robot. In the debate on the grounding issue little or no attention has been paid to the motivational aspects of grounding. The subject area of motivation is central to understanding how an organism is capable of autonomous functioning, i.e. deciding what is important to its continued existence and determining the means of achieving the relevant goals. This paper sets out to examine this aspect of the grounding debate and identifies some of the features which characterize authentic motivational structures in animals and discuss the possibility of implementing similar structures in artificial creatures. It will be argued that an appreciation of the interactive nature of motivation may be the key to understanding motivational grounding. © Elsevier Science B.V. All rights reserved.

关键词： Artificial motivation evolutionary robotics Grounding Incentive learning Motivational indices Value

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Evolving an Artificial Homeostatic System

Evolving an Artificial Homeostatic System

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19th Brazilian Symposium on Artificial Intelligence

作者： Moioli, Renan C. Vargas, Patricia A. Von Zuben, Fernando J. Husbands, Phil Univ Estadual Campinas FEEC Sch Elect & Comp Engineer Lab Bioinformat & Bioinspired Comp Campinas SP Brazil Univ Sussex CCNR Dept Informat Brighton BN1 9QH E Sussex England

ISBN: (纸本)9783540881896

Theory presented by Ashby states that the process of homeostasis is directly related to intelligence and to the ability of an individual in successfully adapting to dynamic environments or disruptions. This paper presents an artificial homeostatic system under evolutionary control, composed of an extended model of the GasNet artificial neural network framework, named NSGasNet, and an artificial endocrine system. Mimicking properties of the neuro-endocrine interaction, the system is shown to be able to properly coordinate the behaviour of a simulated agent that, presents internal dynamics and is devoted to explore the scenario without endangering its essential organization. Moreover, sensorimotor disruptions are applied, impelling the system to adapt in order to maintain some variables within limits, ensuring the agent survival. It is envisaged that the proposed framework is a step towards the design of a generic model for coordinating more complex behaviours, and potentially coping with further severe disruptions.

关键词： evolutionary robotics Homeostasis Adaptation Artificial Neural Networks

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Multi-objective Neuro-evolution: Should the Main Reproduction Mechanism be Crossover or Mutation?

Multi-objective Neuro-evolution: Should the Main Reproductio...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Salih, Adham Moshaiov, Amiram Tel Aviv Univ Sch Mech Engn Tel Aviv Israel ORT Braude Coll Engn Mech Engn Dept Karmiel Israel

ISBN: (纸本)9781509018970

Given the fundamental difference between the selection and reproduction mechanisms of MO-CMA-ES and NSGA-II, it should be asked which kind of these mechanisms is better for the multi-objective evolution of neuro-controllers. This question, which has been recently raised and studied, is further investigated here. The numerical investigation is based on two multi-objective navigation problems, in conjunction with two types of networks. In all the studied cases it was found that MO-CMA-ES is better than NSGA-II. The reason for the superiority is explored. First, it is shown that the competing convention problem cannot serve as an explanation to the observed phenomenon. A method is suggested to investigate the convergence of the networks. Based on the proposed methodology, it is found that for the studied cases MO-CMA-ES has a much better convergence properties. The differences between the two algorithms, and the uniqueness of the considered neuro-evolution problems, lead to the following hypothesis. It is postulated that MO-CMA-ES is superior as a result of its ability to fine-tune the solutions by changing particular genes, each at a time.

关键词： evolutionary neural-network neuroevolution evolutionary robotics multi-objective optimization

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Evolution of Neural Controllers for Simulated and Real Quadruped Robots

Evolution of Neural Controllers for Simulated and Real Quadr...

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2nd International Conference on Robot, Vision and Signal Processing (RVSP)

作者： Farooq, Sehar Shahzad Kim, KyungJoong Sejong Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781479931842

evolutionary robotics is an approach that employs evolutionary computation to develop a controller for an autonomous robotic system. evolutionary computing usually operates depending on a population of candidate controllers, initially selected from a random distribution. The population is iteratively modified according to the fitness function. In this paper, an automatic control system is designed for quadruped robots using an evolutionary Neural Network (ENN) and the performance is measured in terms of the distance travelled by the robot from its origin. The evolved neural controllers are analyzed in the simulation environment and the results are implemented in a real quadruped robot. The comparison between the simulated and real robot shows the performance of the quadruped robot in terms of number of iterations over the distance covered in the desired direction. The developed ENN helps the robot to choose the best possible solution to achieve the maximum distance.

关键词： component evolutionary robotics Quadruped Robot evolutionary Neural Controller Systematic Analysis

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Validation of a Learning and Evolving Robot Swarm 17

Validation of a Learning and Evolving Robot Swarm

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Genetic and evolutionary Computation Conference (GECCO)

作者： Munk, Rasmus Hart, Emma Paechter, Ben Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland

ISBN: (纸本)9781450349390

Recent research in small populations of Thymio II robots illustrated the relative benefits of populations distinguishing heritable and learning features in robots for a simple obstacle avoidance task. Here we scientifically validate these results by repeating them using a simulation. An additional benefit of this work is to provide confidence in the simulation model. This is important because evolutionary swarm robotics experiments can be very time consuming to run in real robots. Having a reliable simulation allows many more experiments to be run in simulation with only the most interesting results needing to be verified with real robots. We describe the development of a simulation using RoboRobo that's using the same three-tier learning framework that was demonstrated in the real-world. The simulation is shown to replicate the real-world results in terms of illustrating the relative benefits of each type of learning, and if anything, indicates that social learning can be more powerful than originally thought.

关键词： evolutionary robotics Simulated robotics Social Learning

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Observing the Effects of Overdesign in the Automatic Design of Control Software for Robot Swarms 10th

Observing the Effects of Overdesign in the Automatic Design ...

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10th International Conference on Swarm Intelligence (ANTS)

作者： Birattari, Mauro Delhaisse, Brian Francesca, Gianpiero Kerdoncuff, Yvon Univ Libre Bruxelles IRIDIA Brussels Belgium ENSTA ParisTech Palaiseau France

ISBN: (纸本)9783319444277;9783319444260

We present the results of an experiment in the automatic design of control software for robot swarms. We conceived the experiment to corroborate a hypothesis that we proposed in a previous publication: the reality gap problem bears strong resemblance to the generalization problem faced in supervised learning. In particular, thanks to this experiment we observe for the first time a phenomenon that we shall call overdesign. Overdesign is the automatic design counterpart of the well known overfitting problem encountered in machine learning. Past an optimal level of the design effort, the longer the design process is protracted, the better the performance of the swarm becomes in simulation and the worst in reality. Our results show that some sort of early stopping mechanism could be beneficial.

关键词： Swarm robotics Automatic design evolutionary robotics Reality gap Generalization Overdesign Early stopping

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Exploring Conflicting Objectives with MADNS: Multiple Assessment Directed Novelty Search

Exploring Conflicting Objectives with MADNS: Multiple Assess...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Smith, Davy Tokarchuk, Laurissa Wiggins, Geraint Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9781450343237

Novelty search is an evolutionary approach which promotes phenotypic diversity in a population. Novelty search has been successfully applied to a wide range of domains and a number of variants have been proposed. Here we introduce Multiple Assessment Directed Novelty Search (MADNS), which exploits the notion that a diverse population optimised through phenotypic novelty may contain solutions to multiple conflicting objectives. We show that by utilising the MADNS algorithm, an evolutionary trajectory may be simultaneously directed towards conflicting objectives. We conclude that, through applying MADNS and MC-MADNS, a divergent evolutionary trajectory may be directed to provide simultaneous solutions to multiple conflicting problems in domains with large potential for exploration.

关键词： Novelty search algorithm design phenotypic diversity neuroevolution evolutionary robotics

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An approach to the synthesis of humanoid robot dance using non-interactive evolutionary techniques.

An approach to the synthesis of humanoid robot dance using n...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Malachy Eaton Dept. Computer Science and Information Systems University of Limerick Limerick Ireland

ISBN: (纸本)9781479906505

After bipedal locomotion, dance is one of the most commonly studied behaviours for researchers seeking to replicate human-like motion in humanoid robots. Many of the methods employed involve direct interaction with, or imitation of, human participant(s). For example, the generation of dance movements using interactive evolutionary computation (IEC) involves the replacement of an objective fitness function with the subjective evaluations of human observer(s). In this paper we present an alternative approach to the synthesis of humanoid robot dance using non-interactive evolutionary computation (non-IEC) methods. We propose a novel fitness function for the evolution of robotic dance, and we present initial results of the application of this evolutionary process to the generation of dance patterns for the 18-DOF Bioloid humanoid robot. We conclude that even without the presence of a human or humans in the evolutionary loop, it is possible to produce surprisingly lifelike and novel dances using this approach.

关键词： genetic algorithms humanoid robots evolutionary robotics dance behaviours Humanoid robots Dancing Genetic Algorithm (GA) Human non-interactive synthesis evolutionary

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