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The dynamic of body and brain co-evolution

ADAPTIVE BEHAVIOR 2022年第3期30卷 245-255页

作者： Pagliuca, Paolo Nolfi, Stefano CNR Inst Cognit Sci & Technol Via S Martino della Battaglia 44 I-00185 Rome Italy

We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morphological bauplan or to evolve the morphological bauplan as well. Our results indicate that robots with co-adapted body and control traits outperform robots with fixed hand-designed morphologies. Interestingly, the advantage is not due to the selection of better morphologies but rather to the mutual scaffolding process that results from the possibility to co-adapt the morphological traits to the control traits and vice versa. Our results also demonstrate that morphological variations do not necessarily have destructive effects on robots' skills.

关键词： Embodiment evolutionary robotics body and brain co-evolution co-adaptation

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The dynamics of associative learning in evolved model circuits

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ADAPTIVE BEHAVIOR 2007年第4期15卷 377-396页

作者： Phattanasri, Phattanard Chiel, Hillel J. Beer, Randall D. Indiana Univ Dept Comp Sci Cognit Sci Program Bloomington IN 47406 USA Case Western Reserve Univ Dept Biol Cleveland OH 44106 USA Case Western Reserve Univ Dept Neurosci Cleveland OH 44106 USA Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA

In this article, we evolve and analyze continuous-time recurrent neural networks capable of associating the smells of different foods with edibility or inedibility in different environments. First, we present an in-depth analysis of this task, highlighting the evolutionary challenges it poses and how these challenges informed our experimental design. Next, we describe the evolution of nonplastic neural circuits that can solve this food edibility learning problem. We then show that the dynamics of the best evolved nonplastic circuits instantiate finite state machines that capture the combinatorial structure of this task. Finally, we demonstrate that successful circuits with Hebbian synaptic plasticity can also be evolved, but that such circuits do not utilize their synaptic plasticity in a traditional way.

关键词： evolution of learning evolutionary robotics dynamical systems theory synaptic plasticity neuroscience

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Evolving morphology automatically reformulates the problem of designing modular control

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ADAPTIVE BEHAVIOR 2018年第2期26卷 47-64页

作者： Bernatskiy, Anton Bongard, Josh Univ Vermont 210 Colchester Ave Burlington VT 05405 USA Univ Vermont Comp Sci Burlington VT USA

Modularity is a system property of many natural and artificial adaptive systems. evolutionary algorithms designed to produce modular solutions have increased convergence rates and improved generalization ability;however, their performance can be impacted if the task is inherently nonmodular. Previously, we have shown that some design variables can influence whether the task on the remaining variables is inherently modular. We investigate the possibility of exploiting that dependence to simplify optimization and arrive at a general design pattern that we use to show that evolutionary search can seek such modularity-inducing design variable values, thus easing subsequent search for highly fit, modular organization within the remaining design variables. We investigate this approach with embodied agents in which evolutionary discovery of morphology enables subsequent discovery of highly fit, modular controllers and show that it benefits from biasing search toward modular controllers and setting the mutation rate for control policies higher than that for morphology. This work also reinforces our previous finding that the relationship between modularity and evolvability that is well-studied in nonembodied systems can, under certain conditions, be generalized to include embodied systems as well and provides a practical approach to satisfying the conditions in question.

关键词： Reformulation modularity genetic algorithm evolvable morphology evolutionary robotics morphological computation

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Symbols and dynamics in the brain

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BIOSYSTEMS 2001年第1-3期60卷 59-83页

作者： Cariani, P Massachusetts Eye & Ear Infirm Eaton Peabody Lab Auditory Physiol Boston MA 02114 USA

The work of physicist and theoretical biologist Howard Pattee has focused on the roles that symbols and dynamics play in biological systems. Symbols, as discrete functional switching-states, are seen at the heart of all biological systems in the form of genetic codes. and at the core of all neural systems in the form of informational mechanisms that switch behavior. They also appear in one form or another in all epistemic systems, from informational processes embedded in primitive organisms to individual human beings to public scientific models. Over its course, Pattee's work has explored (1) the physical basis of informational functions (dynamical vs. rule-based descriptions, switching mechanisms, memory, symbols), (2) the functional organization of the observer (measurement, computation), (3) the means by which information can be embedded in biological organisms fur purposes of self-construction and representation (as codes modeling relations, memory. symbols), and (4) the processes by which new structures and functions can emerge over time. We discuss how these concepts can be applied to a high-level understanding of the brain. Biological organisms constantly reproduce themselves as well as their relations with their environs. The brain similarly can be seen as a self-producing, sell-regenerating neural signaling system and as an adaptive informational system that interacts with its surrounds in order to steer behavior. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.

关键词： adaptive systems biological cybernetics biological semiotics dynamical systems emergence epistemology evolutionary robotics genetic code neural code neurocomputation self-organization symbols

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Effects of Congestion on Swarm Performance and Autonomous Specialization in Robotic Swarms

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JOURNAL OF robotics AND MECHATRONICS 2019年第4期31卷 526-534页

作者： Hiraga, Motoaki Ohkura, Kazuhiro Hiroshima Univ 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan

This paper focuses on the effect of congestion on swarm performance by considering the number of robots and their size. Swarm robotics is the study of a large group of autonomous robots from which collective behavior emerges without reliance on any centralized control. Due to the fact that robotic swarms are composed of a large number of robots, it is important to consider the congestion among them. However, only a few studies have focused on the relationship between the congestion and the performance of robotic swarms;moreover, these studies only discuss the effect of the number of robots. In this study, experiments were conducted by computer simulation and carried out by varying both the number of robots and the size of the robots in a path formation task. The robot controller was designed with an evolutionary robotics approach. The results show that not only the number of robots but also their size are essential features in the relationship between congestion and swarm performance. In addition, autonomous specialization within the robotic swarm emerged in situations with moderate congestion.

关键词： swarm robotics evolutionary robotics congestion autonomous specialization

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Behavioral specialization emerges from the embodiment of a robotic swarm

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ARTIFICIAL LIFE AND robotics 2020年第4期25卷 495-502页

作者： Hiraga, Motoaki Tamura, Yasumasa Ohkura, Kazuhiro Hiroshima Univ Grad Sch Engn Hiroshima Japan Tokyo Inst Technol Sch Comp Dept Comp Sci Tokyo Japan

This paper focuses on the effect of the embodiment of robots on collective behavior in robotic swarms. The research field of swarm robotics emphasizes the importance of the embodiment of robots;however, only a few studies have discussed how it influences the collective behavior of a robotic swarm. In this paper, a path-formation task is performed by robotic swarms in computer simulations with and without considering collisions among robots to discuss the effect of the robot embodiment. Additionally, the experiments were performed with varying the size of robots. The robot controllers were obtained by an evolutionary robotics approach. The results show that the robot collisions would affect not only the performance of the robotic swarm but also the emergent behavior to accomplish the task. The robot collisions seem to provide feedback on robotic swarms to emerge the division of labor among robots to manage congestion.

关键词： Swarm robotics evolutionary robotics Robot collisions Robot embodiment

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Echo state networks for embodied evolution in robotic swarms

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ARTIFICIAL LIFE AND robotics 2023年第1期28卷 139-147页

作者： Hiraga, Motoaki Katada, Yoshiaki Ohkura, Kazuhiro Hiroshima Univ Grad Sch Adv Sci & Engn 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan Setsunan Univ Dept Elect & Elect Engn 17-8 Ikedanaka Machi Neyagawa Osaka 5728508 Japan

Embodied evolution is an evolutionary robotics approach that implements an evolutionary algorithm over a population of robots and evolves while the robots perform their tasks. So far, most studies on embodied evolution utilize relatively simple neural networks as robot controllers. However, a simple structured controller might restrict robot behavior and lead to lower performance. This paper proposes an embodied evolution approach that uses echo state networks as robot controllers. The experiments are conducted using computer simulations, and the controllers are evolved in a two-target navigation task. The results show that the echo state network controllers outperform the conventional controllers.

关键词： Embodied evolution Swarm robotics evolutionary robotics Echo state networks

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Behavior reuse and virtual sensors in the evolution of complex behavior architectures

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THEORY IN BIOSCIENCES 2001年第3-4期120卷 188-206页

作者： Duro, RJ Becerra, JA Santos, J Univ A Coruna Grp Sistemas Autononos La Coruna Spain

In this paper we discuss some of the new work we have been carrying out with the objective of making evolutionarily obtained behavior based architectures and modules for autonomous robots more standardized and interchangeable. The architectures contemplated here are based on a multiple behavior structure where all of the modules, as well as their interconnections, are automatically obtained through evolutionary processes. The main objective of this line of research is to obtain procedures that permit producing behavior based controllers that work on real robots operating in real environments as independently of the platform as possible. In this particular paper we will concentrate on different aspects regarding the inclusion of virtual sensors as a way to make improved use of the capabilities of the different platforms and on the reuse of behavior modules. This reuse will be contemplated within the same behavioral architecture and from the point of view of transferring behavior modules from one platform to a different one.

关键词： autonomy evolutionary robotics behavior reuse behavior based robotics artificial neural networks

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Online evolution for a self-adapting robotic navigation system using evolvable hardware

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ARTIFICIAL LIFE 1998年第4期4卷 359-393页

作者： Keymeulen, D Iwata, M Kuniyoshi, Y Higuchi, T Electrotech Lab Tsukuba Ibaraki 3058568 Japan

Great interest has been shown in the application of the principles of artificial life to physically embedded systems such as mobile robots, computer networks, home devices able continuously and autonomously to adapt their behavior to changes of the environments. At the same time researchers have been working on the development of evolvable hardware, and new integrated circuits that are able to adapt their hardware autonomously and in real time in a changing environment. This article describes the navigation task for a real mobile robot and its implementation on evolvable hardware. The robot must track a colored ball, while avoiding obstacles in an environment that is unknown and dynamic. Although a model-free evolution method is not feasible for real-world applications due to the sheer number of possible interactions with the environment, we show that a model-based evolution can reduce these interactions by two orders of magnitude, even when some of the robot's sensors are blinded, thus allowing us to apply evolutionary processes online to obtain a self-adaptive tracking system in the real world, when the implementation is accelerated by the utilization of evolvable hardware.

关键词： evolutionary robotics evolvable hardware embedded system genetic algorithm adaptive sensorimotor control

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Associative Learning on a Continuum in Evolved Dynamical Neural Networks

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ADAPTIVE BEHAVIOR 2008年第6期16卷 361-384页

作者： Izquierdo, Eduardo Harvey, Inman Beer, Randall D. Univ Sussex Dept Informat Ctr Computat Neurosci & Robot Brighton BN1 9QG E Sussex England Indiana Univ Dept Comp Sci Cognit Sci Program Dept Informat Bloomington IN 47406 USA

This article extends previous work on evolving learning without synaptic plasticity from discrete tasks to continuous tasks. Continuous-time recurrent neural networks without synaptic plasticity are artificially evolved on an associative learning task. The task consists in associating paired stimuli: temperature and food. The temperature to be associated can be either drawn from a discrete set or allowed to range over a continuum of values. We address two questions: Can the learning without synaptic plasticity approach be extended to continuous tasks? And if so, how does learning without synaptic plasticity work in the evolved circuits? Analysis of the most successful circuits to learn discrete stimuli reveal finite state machine (FSM) like internal dynamics. However, when the task is modified to require learning stimuli on the full continuum range, it is not possible to extract a FSM from the internal dynamics. In this case, a continuous state machine is extracted instead.

关键词： associative learning evolutionary robotics dynamical systems theory synaptic plasticity evolution of learning

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