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Titlebook: Intrinsically Motivated Learning in Natural and Artificial Systems; Gianluca Baldassarre,Marco Mirolli Book 2013 Springer-Verlag Berlin He

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发表于 2025-3-21 17:18:27 | 显示全部楼层 |阅读模式
书目名称Intrinsically Motivated Learning in Natural and Artificial Systems
编辑Gianluca Baldassarre,Marco Mirolli
视频video
概述Interdisciplinary authors explain latest theories on mammalian intelligence and learning, artificial intelligence, creativity, and evolution Identifies scientific and technological open challenges and
图书封面Titlebook: Intrinsically Motivated Learning in Natural and Artificial Systems;  Gianluca Baldassarre,Marco Mirolli Book 2013 Springer-Verlag Berlin He
描述.It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and inter­est in learning new behaviours. The adaptive value of such intrinsically motivated activities lies in the fact that they allow the cumulative acquisition of knowledge and skills that can be used later to accomplish fitness-enhanc­ing goals. Intrinsic motivations continue during adulthood, and in humans they underlie lifelong learning, artistic creativity, and scientific discovery, while they are also the basis for processes that strongly affect human well-being, such as the sense of competence, self-determination, and self-esteem..This book has two aims: to present the state of the art in research on intrinsically motivated learning, and to identif
出版日期Book 2013
关键词artificial intelligence; artificial life; cognition; creativity; development; embodiment; evolution; fun; hu
版次1
doihttps://doi.org/10.1007/978-3-642-32375-1
isbn_softcover978-3-642-44293-3
isbn_ebook978-3-642-32375-1
copyrightSpringer-Verlag Berlin Heidelberg 2013
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发表于 2025-3-21 23:30:39 | 显示全部楼层
Exploration from Generalization Mediated by Multiple Controllersd that imply potential benefits from generalizing knowledge and skills to subsequent environments. We discuss how functionally and architecturally different controllers may realize these bonuses in different ways.
发表于 2025-3-22 02:09:40 | 显示全部楼层
Novelty and Beyond: Towards Combined Motivation Models and Integrated Learning Architectureshitectures are discussed that combine basic units of the intrinsic motivation functions in different ways. This chapter concludes with a discussion of future directions for combined motivation models and integrated learning architectures.
发表于 2025-3-22 05:53:29 | 显示全部楼层
Investigating the Origins of Intrinsic Motivation in Human Infants develops. Next, I describe work on the development of perceptual completion, which offers a case study on the development of visual exploration and the role of oculomotor skill. I conclude by discussing a number of challenges and open questions that are suggested by this work.
发表于 2025-3-22 11:04:40 | 显示全部楼层
Intrinsically Motivated Learning Systems: An Overview,f the aforementioned intrinsic-motivation mechanisms. The contributions of the fifth part discuss mechanisms that are complementary to intrinsic motivations. The contributions of the sixth part introduce tools and experimental paradigms that can be used to investigate intrinsic motivations.
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发表于 2025-3-23 00:18:19 | 显示全部楼层
Action Discovery and Intrinsic Motivation: A Biologically Constrained Formalisation“surprise”). The notion of salience as value is introduced and broken down into contributions from novelty (or surprise), immediate reward acquisition, or general task/goal attainment. Many other aspects of biological action discovery emerge naturally in our framework which aims to guide future modelling efforts in this domain.
发表于 2025-3-23 01:29:57 | 显示全部楼层
Intrinsically Motivated Affordance Discovery and Modelingude strategies for finding, grasping, and placing simple objects. We further show how this same intrinsic reward function is used to direct the robot to build stable models of when the environment affords these skills.
发表于 2025-3-23 07:24:29 | 显示全部楼层
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