检索结果-内蒙古大学图书馆

Indirect genetic effects allow escape from the inefficient equilibrium in a coordination game

EVOLUTION LETTERS 2020年第3期4卷 257-265页

作者： Bernard, Arthur Bredeche, Nicolas Andre, Jean-Baptiste Sorbonne Univ CNRS Inst Syst Intelligents & Robot F-75005 Paris France Univ Lausanne Dept Ecol & Evolut CH-1015 Lausanne Switzerland PSL Res Univ CNRS Inst Jean Nicod Dept Etud CognitENSEHESS Paris France

Social interactions involving coordination between individuals are subject to an "evolutionary trap." Once a suboptimal strategy has evolved, mutants playing an alternative strategy are counterselected because they fail to coordinate with the majority. This creates a detrimental situation from which evolution cannot escape, preventing the evolution of efficient collective behaviors. Here, we study this problem using evolutionary robotics simulations. We first confirm the existence of an evolutionary trap in a simple setting. We then, however, reveal that evolution can solve this problem in a more realistic setting where individuals need to coordinate with one another. In this setting, simulated robots evolve an ability to adapt plastically their behavior to one another, as this improves the efficiency of their interaction. This ability has an unintended evolutionary consequence: a genetic mutation affecting one individual's behavior also indirectly alters their partner's behavior because the two individuals influence one another. As a consequence of this indirect genetic effect, pairs of partners can virtually change strategy together with a single mutation, and the evolutionary barrier between alternative strategies disappears. This finding reveals a general principle that could play a role in nature to smoothen the transition to efficient collective behaviors in all games with multiple equilibriums.

关键词： Collective action evolution of cooperation evolutionary robotics evolutionary game theory equilibrium selection plasticity

来源：评论

学校读者我要写书评

暂无评论

Shrunken Social Brains? A Minimal Model of the Role of Social Interaction in Neural Complexity

引用

FRONTIERS IN NEUROrobotics 2021年 15卷 634085页

作者： Resendiz-Benhumea, Georgina Montserrat Sangati, Ekaterina Sangati, Federico Keshmiri, Soheil Froese, Tom Grad Univ Embodied Cognit Sci Unit Okinawa Inst Sci & Technol Okinawa Japan Univ Nacl Autonoma Mexico Inst Appl Math & Syst Res Comp Sci & Engn Postgrad Program Mexico City DF Mexico

The social brain hypothesis proposes that enlarged brains have evolved in response to the increasing cognitive demands that complex social life in larger groups places on primates and other mammals. However, this reasoning can be challenged by evidence that brain size has decreased in the evolutionary transitions from solitary to social larger groups in the case of Neolithic humans and some eusocial insects. Different hypotheses can be identified in the literature to explain this reduction in brain size. We evaluate some of them from the perspective of recent approaches to cognitive science, which support the idea that the basis of cognition can span over brain, body, and environment. Here we show through a minimal cognitive model using an evolutionary robotics methodology that the neural complexity, in terms of neural entropy and degrees of freedom of neural activity, of smaller-brained agents evolved in social interaction is comparable to the neural complexity of larger-brained agents evolved in solitary conditions. The nonlinear time series analysis of agents' neural activity reveals that the decoupled smaller neural network is intrinsically lower dimensional than the decoupled larger neural network. However, when smaller-brained agents are interacting, their actual neural complexity goes beyond its intrinsic limits achieving results comparable to those obtained by larger-brained solitary agents. This suggests that the smaller-brained agents are able to enhance their neural complexity through social interaction, thereby offsetting the reduced brain size.

关键词： agent-based modeling social interaction complexity entropy social brains evolutionary robotics continuous-time recurrent neural network nonlinear time series analysis

来源：评论

学校读者我要写书评

暂无评论

Distributed On-line Learning in Swarm robotics with Limited Communication Bandwidth

Distributed On-line Learning in Swarm Robotics with Limited ...

引用

IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Fontbonne, Nicolas Dauchot, Olivier Bredeche, Nicolas Sorbonne Univ Inst Syst Intelligents & Robot CNRS Paris France ESPCI CNRS Gulliver Lab Paris France

ISBN: (纸本)9781728169293

This paper presents a new algorithm for distributed on-line evolutionary learning in swarm robotics. The challenge we address is to cope with the limited computation and communication capabilities of low cost robots, which are often used in swarm robotics. In order to do so, the algorithm decouples computation and communication and ensures learning of efficient control policies even when only a limited amount of information can be exchanged between neighbouring robots. We show experimentally that this algorithm is both remarkably robust with respect to its meta-parameter values, and able to adapt automatically to the available communication bandwidth.

关键词： online distributed learning swarm robotics black-box objective function embodied evolution social learning evolutionary robotics

来源：评论

学校读者我要写书评

暂无评论

Evolving Spiking Neurocontrollers for UAVs

Evolving Spiking Neurocontrollers for UAVs

引用

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Qiu, Huanneng Garratt, Matthew Howard, David Anavatti, Sreenatha Univ New South Wales Canberra Sch Engn & Informat Technol Canberra ACT Australia CSIRO Robot & Autonomous Syst Grp Brisbane Qld Australia

ISBN: (纸本)9781728125473

Spiking neural networks (SNNs) are neuroscience-inspired computational systems that carry out computation based on the biological modeling of neuron interactions. Current SNN studies have shown their ability to solve a wide variety of machine learning problems. The temporal dynamics and future low-power neuromorphic implementations of SNNs also make them suitable controller candidates for embedded applications, especially for robotic platforms with very low payload and power budgets (e.g. Micro Air Vehicles). In this paper, we present a solution to simulate full control of a hexacopter UAV in 6 degrees of freedom using SNNs. By decomposing the neurocontroller into modules, we demonstrate that the development of UAV Bight control can be accomplished by an incremental evolutionary approach using a modified NEAT algorithm.

关键词： spiking neural networks neuroevolution NEAT UAV evolutionary robotics

来源：评论

学校读者我要写书评

暂无评论

Evolving-Controllers Versus Learning-Controllers for Morphologically Evolvable Robots 1

引用

23rd European International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Miras, Karine De Carlo, Matteo Akhatou, Sayfeddine Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

We investigate an evolutionary robot system where (simulated) modular robots can reproduce and create robot children that inherit the parents' morphologies by crossover and mutation. Within this system we compare two approaches to creating good controllers, i.e., evolution only and evolution plus learning. In the first one the controller of a robot child is inherited, so that it is produced by applying crossover and mutation to the controllers of its parents. In the second one the controller of the child is also inherited, but additionally, it is enhanced by a learning method. The experiments show that the learning approach does not only lead to different fitness levels, but also to different (bigger) robots. This constitutes a quantitative demonstration that changes in brains, i.e., controllers, can induce changes in the bodies, i.e., morphologies.

关键词： Morphological evolution Life-time learning evolutionary robotics Modular robots

来源：评论

学校读者我要写书评

暂无评论

Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers

Towards Crossing the Reality Gap with Evolved Plastic Neuroc...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Qiu, Huanneng Garratt, Matthew Howard, David Anavatti, Sreenatha Univ New South Wales Canberra Canberra ACT Australia CSIRO Robot & Autonomous Syst Grp Brisbane Qld Australia

ISBN: (纸本)9781450371285

A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as the platforms are highly dynamic and susceptible to breakage. Previous approaches often require simulation models with a high level of accuracy, otherwise significant errors may arise when the well-designed controller is being deployed onto the targeted platform. Here we try to overcome the transfer problem from a different perspective, by designing a spiking neurocontroller which uses synaptic plasticity to cross the reality gap via online adaptation. Through a set of experiments we show that the evolved plastic spiking controller can maintain its functionality by self-adapting to model changes that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart.

关键词： evolutionary robotics spiking neural networks Hebbian plasticity neuroevolution UAV control

来源：评论

学校读者我要写书评

暂无评论

Evolution of Distributed Neural Controllers for Voxel-based Soft Robots

Evolution of Distributed Neural Controllers for Voxel-based ...

引用

Genetic and evolutionary Computation Conference (GECCO)

作者： Medvet, Eric Bartoli, Alberto De Lorenzo, Andrea Fidel, Giulio Univ Trieste DIA Trieste Italy

ISBN: (纸本)9781450371285

Voxel-based soft robots (VSRs) are aggregations of elastic, cubic blocks that have sparkled the interest of robotics and Artificial Life researchers. VSRs can move by varying the volume of individual blocks, according to control signals dictated by a controller, possibly based on inputs coming from sensors embedded in the blocks. Neural networks (NNs) have been used as centralized processing units for those sensing controllers, with weights optimized using evolutionary computation. This structuring breaks the intrinsic modularity of VSRs: decomposing a VSR into modules to be assembled in a different way is very hard. In this work we propose an alternative approach that enables full modularity and is based on a distributed neural controller. Each block contains a small NN that outputs signals to adjacent blocks and controls the local volume, based on signals from adjacent blocks and on local sensor readings. We show experimentally for the locomotion task that our controller is as effective as the centralized one. Our experiments also suggest that the proposed framework indeed allows exploiting modularity: VSRs composed of pre-trained parts (body and controller) can be evolved more efficiently than starting from scratch.

关键词： evolutionary robotics Modularity Neuroevolution

来源：评论

学校读者我要写书评

暂无评论

Localization Uncertainty-driven Adaptive Framework for Controlling Ground Vehicle Robots

Localization Uncertainty-driven Adaptive Framework for Contr...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kent, Daniel McKinley, Philip K. Radha, Hayder Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI USA

ISBN: (纸本)9781728162126

Modern localization techniques allow ground vehicle robots to determine their position with centimeter-level accuracy under nominal conditions, enabling them to utilize fixed maps to navigate their environments. However, when localization measurements become unavailable, the position accuracy will drop and uncertainty will increase. While research and development on localization estimation seeks to reduce the severity of these outages, the question of what actions a robot should take under high localization uncertainty is still unresolved, and can vary on a platform-by-platform and mission-by-mission basis. In this paper, we exploit localization uncertainty measures to adapt system control parameters in real time. Offline, we optimize non-linear activation functions whose control parameters and relevant weights are trained and learned using evolutionary Algorithm (EA). Subsequently, in real time, we apply the optimized adaptation functions to the controller look-ahead distance and intermediate linear and angular velocity commands, which we identify as the most sensitive to localization error. evolutionary runs are conducted in which a simulated target vehicle is tasked with following a randomly generated path while minimizing cross-track error, with time varying localization uncertainty added. These runs produce situation-dependent weights for parameters to the adaptation functions, which are transferred to the physical platform, a 1:5-scale autonomous vehicle. In simulation, our system was able to reduce cross-track error, which in certain cases exceeds 250 centimeters on non-adapted systems, to below 15 centimeters on average using EA-derived weights and parameters applied to our proposed adaptation system. Evaluation on the physical platform demonstrates that without the adaptation module in place, the platform is unable to successfully follow the path;with the adaptation module, the platform automatically adjusts its velocity and look-ahead distance to compensat

关键词： autonomous vehicle localization path planning uncertainty evolutionary robotics Robot Operating System

来源：评论

学校读者我要写书评

暂无评论

Evolving Robot Software and Hardware 15

Evolving Robot Software and Hardware

引用

IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

作者： Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450379625

This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary way of creating 'bodies' and 'brains' of robots for engineering and fundamental research.

关键词： evolutionary Computation evolutionary robotics Learning

来源：评论

学校读者我要写书评

暂无评论

Behavioral Repertoires for Soft Tensegrity Robots

Behavioral Repertoires for Soft Tensegrity Robots

引用

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Doney, Kyle Petridou, Aikaterini Karaul, Jacob Khan, Ali Liu, Geoffrey Rieffel, John Union Coll Comp Sci Dept Schenectady NY 12308 USA

ISBN: (纸本)9781728125473

Mobile soft robots offer compelling applications in fields ranging from urban search and rescue to planetary exploration. A critical challenge of soft robotic control is that the nonlinear dynamics imposed by soft materials often result in complex behaviors that are counter-intuitive and hard to model or predict. As a consequence, most behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. A second challenge is that soft materials are difficult to simulate with high fidelity - leading to a significant reality gap when trying to discover or optimize new behaviors. In this work we employ a Quality Diversity Algorithm running model-free on a physical soft tensegrity robot that autonomously generates a behavioral repertoire with no a priori knowledge of the robot's dynamics, and minimal human intervention. The resulting behavior repertoire displays a diversity of unique locomotive gaits useful for a variety of tasks. These results help provide a road map for increasing the behavioral capabilities of mobile soft robots through real-world automation.

关键词： Soft robotics Quality Diversity Algorithms Tensegrity evolutionary robotics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：