標(biāo)題: Titlebook: Computational and Robotic Models of the Hierarchical Organization of Behavior; Gianluca Baldassarre,Marco Mirolli Book 2013 Springer-Verla [打印本頁(yè)] 作者: 初生 時(shí)間: 2025-3-21 16:43
書(shū)目名稱Computational and Robotic Models of the Hierarchical Organization of Behavior影響因子(影響力)
書(shū)目名稱Computational and Robotic Models of the Hierarchical Organization of Behavior影響因子(影響力)學(xué)科排名
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書(shū)目名稱Computational and Robotic Models of the Hierarchical Organization of Behavior網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Computational and Robotic Models of the Hierarchical Organization of Behavior被引頻次
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書(shū)目名稱Computational and Robotic Models of the Hierarchical Organization of Behavior讀者反饋
書(shū)目名稱Computational and Robotic Models of the Hierarchical Organization of Behavior讀者反饋學(xué)科排名
作者: Adornment 時(shí)間: 2025-3-21 20:31
Behavioral Hierarchy: Exploration and Representation ranges of behavior. Hierarchies of behavioral modules facilitate learning complex skills and planning at multiple levels of abstraction and enable agents to incrementally improve their competence for facing new challenges that arise over extended periods of time. This chapter focuses on two feature作者: 大罵 時(shí)間: 2025-3-22 04:28
Self-Organized Functional Hierarchy Through Multiple Timescales: Neuro-dynamical Accounts for Behavies. The models have been examined through robot experiments for the purpose of exploring novel phenomena appearing in the interaction between neural dynamics and physical actions, which could provide us new insights to understand nontrivial brain mechanisms. Those robot experiments successfully show作者: 廢墟 時(shí)間: 2025-3-22 06:53
Autonomous Representation Learning in a Developing Agentons through interaction with the environment. This chapter focuses on the problem of learning representations. We present four principles for autonomous learning of representations in a developing agent, and we demonstrate how these principles can be embodied in an algorithm. In a simulated environm作者: disciplined 時(shí)間: 2025-3-22 08:52
Hierarchies for Embodied Action Perceptionge, and physical, perceptual and computational constraints. This capability relies on action perception mechanisms that exploit regularities in observed goal-oriented behaviours to generate robust predictions and reduce the workload of sensing systems. To achieve this essential capability, we argue 作者: 斗爭(zhēng) 時(shí)間: 2025-3-22 14:21
Learning and Coordinating Repertoires of Behaviors with Common Reward: Credit Assignment and Module ing multiple concurrent goals such as foraging for different foods while avoiding different predators and looking for a mate. A promising way to do so is reinforcement learning (RL) as it considers in a very general way the problem of choosing actions in order to maximize a measure of cumulative ben作者: 斗爭(zhēng) 時(shí)間: 2025-3-22 19:26 作者: 兇殘 時(shí)間: 2025-3-22 22:20
Generalization and Interference in Human Motor Controlace two fundamental issues: (1) they must acquire new skills in a ., that is exploiting previous knowledge to learn new behaviors, and (2) they must avoid the so-called ., where learning new knowledge destroys existing memories. Here, we analyze the problem from the perspective of biological motor c作者: disciplined 時(shí)間: 2025-3-23 03:13
A Developmental Framework for Cumulative Learning Robotsbehaviour and our ability to implement developmental processes in autonomous agents. In this chapter we describe an approach towards developmental growth for robotics that utilises natural constraints in a general learning mechanism. The method, summarised as Lift-Constraint, Act, Saturate (LCAS), i作者: faddish 時(shí)間: 2025-3-23 09:01
The Hierarchical Accumulation of Knowledge in the Distributed Adaptive Control Architecture converted into information that is converted into knowledge. Moreover, theories on cumulative learning suggest that different cognitive layers accumulate this knowledge, building highly complex skills from low complexity ones. The biologically based Distributed Adaptive Control cognitive architectu作者: 性冷淡 時(shí)間: 2025-3-23 12:40 作者: 侵略 時(shí)間: 2025-3-23 17:47
Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humansr, the simple forms of RL considered in most empirical research do not scale well, making their relevance to complex, real-world behavior unclear. In computational RL, one strategy for addressing the scaling problem is to introduce hierarchical structure, an approach that has intriguing parallels wi作者: 使成整體 時(shí)間: 2025-3-23 21:59
Neural Network Modelling of Hierarchical Motor Function in the Brainsible neural network architectures with local associative synaptic learning rules. The chapter begins with a review of our own laboratory’s work in this area. We present a series of hierarchical motor models and relate these to various areas of brain function. This is followed by a discussion of the作者: UTTER 時(shí)間: 2025-3-24 01:55 作者: 和音 時(shí)間: 2025-3-24 06:04 作者: 可耕種 時(shí)間: 2025-3-24 08:37 作者: 歡笑 時(shí)間: 2025-3-24 11:30
https://doi.org/10.1007/978-1-0716-1916-2tion mechanisms, underlying the agent’s ability to add new behaviours to its repertoire. Based on these factors, we review multiple instantiations of a hierarchically-organised biologically-inspired framework for embodied action perception, demonstrating its flexibility in addressing the rich comput作者: Ventilator 時(shí)間: 2025-3-24 17:55 作者: ventilate 時(shí)間: 2025-3-24 20:52 作者: 過(guò)多 時(shí)間: 2025-3-25 01:03
Jing Xue,Annie Quan,Phillip J. Robinsoned on its highest cognitive layer where knowledge is constructed and used. We investigate the roles of reactive and contextual control depending on the characteristics and complexity of the tasks. We also show how multi-sensor information could be integrated in order to acquire and use knowledge opt作者: HAVOC 時(shí)間: 2025-3-25 06:51 作者: habitat 時(shí)間: 2025-3-25 08:05
Justin L. Balsor,Kathryn M. Murphymplished by identifying useful subgoal states, and that this might in turn be accomplished through a structural analysis of the given task domain. We review results from a set of behavioral and neuroimaging experiments, in which we have investigated the relevance of these ideas to human learning and作者: 最高峰 時(shí)間: 2025-3-25 11:40 作者: abreast 時(shí)間: 2025-3-25 17:11
Computational and Robotic Models of the Hierarchical Organization of Behavior作者: 冬眠 時(shí)間: 2025-3-25 21:20
Computational and Robotic Models of the Hierarchical Organization of Behavior978-3-642-39875-9作者: dictator 時(shí)間: 2025-3-26 00:17
Computational and Robotic Models of the Hierarchical Organization of Behavior: An Overview, this subject, the open challenges, and promising research directions. Together, the contributions give a good coverage of the most important models, findings, and challenges of the field. This introductory chapter presents the general aims and scope of the book and briefly summarises the contents o作者: 確定 時(shí)間: 2025-3-26 06:17
Hierarchies for Embodied Action Perceptiontion mechanisms, underlying the agent’s ability to add new behaviours to its repertoire. Based on these factors, we review multiple instantiations of a hierarchically-organised biologically-inspired framework for embodied action perception, demonstrating its flexibility in addressing the rich comput作者: 確認(rèn) 時(shí)間: 2025-3-26 12:10
Learning and Coordinating Repertoires of Behaviors with Common Reward: Credit Assignment and Module ther times avoidance of several predators may be required. The individual modules have separate state representations, i.e. they obtain different inputs but have to carry out actions jointly in the common action space of the agent. Only a single measure of success is observed, which is the sum of th作者: LINE 時(shí)間: 2025-3-26 16:35 作者: oracle 時(shí)間: 2025-3-26 17:22
The Hierarchical Accumulation of Knowledge in the Distributed Adaptive Control Architectureed on its highest cognitive layer where knowledge is constructed and used. We investigate the roles of reactive and contextual control depending on the characteristics and complexity of the tasks. We also show how multi-sensor information could be integrated in order to acquire and use knowledge opt作者: 走調(diào) 時(shí)間: 2025-3-27 00:48
The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Revil picture that emerges is that the cortical and the basal ganglia systems form two highly-organised hierarchical systems working in close synergy and jointly solving all the challenges of choice, selection, and implementation needed to acquire and express adaptive behaviour.作者: cochlea 時(shí)間: 2025-3-27 01:22
Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humansmplished by identifying useful subgoal states, and that this might in turn be accomplished through a structural analysis of the given task domain. We review results from a set of behavioral and neuroimaging experiments, in which we have investigated the relevance of these ideas to human learning and作者: Serenity 時(shí)間: 2025-3-27 06:52 作者: eardrum 時(shí)間: 2025-3-27 09:34
Book 2013 of the brain. They might even lead to the cumulative acquisition of an ever-increasing number of skills, which seems to be a characteristic of mammals, and humans in particular..This book is a comprehensive overview of the state of the art on the modeling of the hierarchical organization of behavio作者: Palatial 時(shí)間: 2025-3-27 14:19
Book 2013t of control architectures and learning algorithms that can support the acquisition and deployment of several different skills, which in turn seems to require a modular and hierarchical organization. In this way, different modules can acquire different skills without catastrophic interference, and h作者: 失誤 時(shí)間: 2025-3-27 20:24 作者: cuticle 時(shí)間: 2025-3-27 22:30
Panayiotis Tsokas,Robert D. Blitzercal reinforcement learning to illustrate the influence of behavioral hierarchy on exploration and representation. Beyond illustrating these features, the examples provide support for the central role of behavioral hierarchy in development and learning for both artificial and natural agents.作者: APNEA 時(shí)間: 2025-3-28 03:03
Behavioral Hierarchy: Exploration and Representationcal reinforcement learning to illustrate the influence of behavioral hierarchy on exploration and representation. Beyond illustrating these features, the examples provide support for the central role of behavioral hierarchy in development and learning for both artificial and natural agents.作者: Rotator-Cuff 時(shí)間: 2025-3-28 09:15
Peter R. Dunkley,Phillip J. Robinsonand interference is examined together with some interpretations in terms of computational models. Finally, we present some possible approaches to the issue of learning multiple tasks while avoiding catastrophic interference in bio-inspired learning architectures.作者: 粗鄙的人 時(shí)間: 2025-3-28 13:06
Generalization and Interference in Human Motor Controland interference is examined together with some interpretations in terms of computational models. Finally, we present some possible approaches to the issue of learning multiple tasks while avoiding catastrophic interference in bio-inspired learning architectures.作者: 止痛藥 時(shí)間: 2025-3-28 17:45 作者: Analogy 時(shí)間: 2025-3-28 18:46 作者: 債務(wù) 時(shí)間: 2025-3-29 02:25 作者: 只有 時(shí)間: 2025-3-29 04:19
Justin L. Balsor,Kathryn M. Murphyis area. We present a series of hierarchical motor models and relate these to various areas of brain function. This is followed by a discussion of the limitations of these models and directions for future research.作者: champaign 時(shí)間: 2025-3-29 08:18
Self-Organized Functional Hierarchy Through Multiple Timescales: Neuro-dynamical Accounts for Behaviynamics and physical actions, which could provide us new insights to understand nontrivial brain mechanisms. Those robot experiments successfully showed us how a set of behavior primitives can be learned with distributed neural activity and how functional hierarchy can be developed for manipulating these primitives in a compositional manner.作者: 不能仁慈 時(shí)間: 2025-3-29 14:39
Autonomous Representation Learning in a Developing Agentus learning of representations in a developing agent, and we demonstrate how these principles can be embodied in an algorithm. In a simulated environment with realistic physics, we show that an agent can use these principles to autonomously learn useful representations and effective hierarchical actions.作者: ADORE 時(shí)間: 2025-3-29 15:49 作者: indicate 時(shí)間: 2025-3-29 22:03
A Developmental Framework for Cumulative Learning Robotss described and illustrated with results from experiments. We discuss how this approach is grounded in the topics of sensory-motor abstraction, intrinsic motivation (as novelty), and staged learning, and our belief that robotics can learn much from infant psychology.作者: Leaven 時(shí)間: 2025-3-30 01:16 作者: 發(fā)牢騷 時(shí)間: 2025-3-30 06:19 作者: filial 時(shí)間: 2025-3-30 11:01 作者: 移動(dòng) 時(shí)間: 2025-3-30 13:41
https://doi.org/10.1007/978-1-0716-1916-2ons through interaction with the environment. This chapter focuses on the problem of learning representations. We present four principles for autonomous learning of representations in a developing agent, and we demonstrate how these principles can be embodied in an algorithm. In a simulated environm
