Communicative capital: a key resource for human-machine shared agency and collaborative capacity.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2022-11-14 DOI:10.1007/s00521-022-07948-1
Kory W Mathewson, Adam S R Parker, Craig Sherstan, Ann L Edwards, Richard S Sutton, Patrick M Pilarski
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引用次数: 2

Abstract

In 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 collaborations have 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.

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交流资本:人机共享代理和协作能力的关键资源。
在这项工作中,我们对机器智能在支持人类能力方面的作用提出了一个观点。特别是,我们考虑对假肢装置等康复技术的研究,因为这一领域需要人和机器之间的紧密耦合。从基于代理的角度来看,我们提出人机协作具有执行任务的能力,这是人和机器联合代理的结果。我们介绍了交流资本,它是由人和机器在不断的互动中共同开发的资源。这种资源的开发使伙伴关系最终能够以比任何一个人单独完成的能力都更大的能力执行任务。然后,我们通过调查文献来研究增加假肢代理的好处和挑战,这些文献表明,建立沟通资源可以实现更复杂的、任务导向的互动。本文提出的观点扩展了当前关于如何最好地支持日益复杂的假肢的功能使用的思考,并为在人类与支持性、辅助性和增强性技术之间创造更富有成效的互动奠定了基础。
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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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