[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-638733-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-638733-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","neural-computing-and-applications-human-aligned-reinforcement-learning-for-autonomous-agents-and-robots-communicative-capital-a-key-resource-for-humanmachine-shared-agency-and-collaborative-capacity","Neural Computing and Applications - HUMAN-ALIGNED REINFORCEMENT LEARNING FOR AUTONOMOUS AGENTS AND ROBOTS - Communicative capital: a key resource for human–machine shared agency and collaborative capacity","","This work presents a perspective on how machine intelligence can support human abilities, emphasizing rehabilitation technologies that require tight coupling between people and machines. Using an agent-based view of prosthetic devices, it introduces communicative capital as a shared resource developed through ongoing human–machine interactions. Building this resource can let the partnership achieve task capacity beyond either agent alone. 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It includes accumulated knowledge, conventions, beliefs, models, and predictions stored in both agents’ memories.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Why does the paper focus on reinforcement learning for autonomous agents and robots?",{"text":67,"@type":63},"It argues that a human’s ability to have, seek, and achieve goals can be supported by combining human ability with reinforcement learning agents, leading to greater potential capacity for shared tasks.",{"name":69,"@type":60,"acceptedAnswer":70},"How do prosthetic devices benefit from increasing their agency?",{"text":71,"@type":63},"The paper examines benefits and challenges of increasing prosthesis agency by surveying literature. 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I : HUMAN-ALIGNED REINFORCEMENT LEARNING FOR AUTONOMOUS AGENTS AND ROBOTS  \nCommunicative capital: a key resource for human–machine shared agency and collaborative capacity  \nKory W. Mathewson 1,2  • Adam S. R. Parker2,3 • Craig Sherstan4 • Ann L. Edwards2 • Richard S. Sutton1,2,3,5 • Patrick M. Pilarski1,2,3,5  \nReceived: 25 February 2022/Accepted: 12 October 2022  \n􀀂 The Author(s) 2022  \nAbstract  \nIn this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human–machine collaborationshave a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce communicative capital as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.  \nKeywords Communicative capital 􀀂 Machine intelligence 􀀂 Prostheses 􀀂 Prosthetic devices 􀀂 Human–machine interaction 􀀂 Agency 􀀂 Prediction learning 􀀂 Communication 􀀂 Rehabilitation technology 􀀂 Robotics  \n1 Introduction  \nTechnology can be used to amplify natural human abilities, provide access to new abilities, and supplement abilities changed due to injury or illness [1–6] . Various tools and technological interventions are well known to support humans in physically interacting with their world, improving perceptual abilities, and supporting decision-  \n& Kory W. Mathewson [korymath@deepmind.com](korymath@deepmind.com)  \n1 DeepMind, Montreal, Canada  \n2 University of Alberta, Edmonton, Canada  \n3 Alberta Machine Intelligence Institute (Amii), Edmonton, Canada  \n4 Sony AI, Tokyo, Japan  \n5 DeepMind, Edmonton, Canada  \nmaking and memory [1, 7–9] . Interventions to provide people with the functions they require for daily life are a core area of interest in rehabilitation, as outlined by the International Classification of Functioning, Disability and Health (ICF) [10, 11] . For example, Geary [1] describes ways that technology is used to enhance sight, touch, hearing, taste, smell, and mental processes. Mill´an et al.  \n[12], Castellini et al. [13], and Carmena [14] further present views on the use of technology to supplement and enhance motor and sensory abilities for people who have lost body parts or body functions. Of interest to this work are technological advances in assistive or augmentative technology involving tight coupling [15] between a person and a machine with the capacity to learn. This coupling affects the ability of the combined human–machine partnership to have, seek, and achieve goals.  \nWe present the perspective that a human’s ability to have, seek, and achieve goals can be supported using  \n1 3  \nmachine intelligence, specifically by combining human ability with reinforcement learning agents [16] . We term this human–machine shared agency. This perspective suggests that a human and their machine counterpart should be viewed as partners attempting to accomplish a shared task, where the agency of each partner combines to allow for greater potential capacity to accomplish tasks","cbCaigMVMok9zVBs","https://ap.wps.com/l/cbCaigMVMok9zVBs","pdf",1147344,15,"English","# Abstract\n# 1 Introduction\n# 2 Robotic upper-limb prostheses","[{\"question\":\"What is communicative capital in human–machine shared agency?\",\"answer\":\"Communicative capital is a resource built over time in a human–machine partnership through ongoing interaction. It includes accumulated knowledge, conventions, beliefs, models, and predictions stored in both agents’ memories.\"},{\"question\":\"Why does the paper focus on reinforcement learning for autonomous agents and robots?\",\"answer\":\"It argues that a human’s ability to have, seek, and achieve goals can be supported by combining human ability with reinforcement learning agents, leading to greater potential capacity for shared tasks.\"},{\"question\":\"How do prosthetic devices benefit from increasing their agency?\",\"answer\":\"The paper examines benefits and challenges of increasing prosthesis agency by surveying literature. It highlights that building communicative resources enables more complex, task-directed interactions.\"}]","Neural Computing and Applications - HUMAN-ALIGNED REINFORCEMENT LEARNING FOR AUTONOMOUS AGENTS AND ROBOTS - Communicative capital: a key resource for human–machine shared agency and collaborative capacity | PDF",38]