更好的环境造就更好的人工智能

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2024-08-07 DOI:10.1002/aaai.12187
Sarah Keren
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引用次数: 0

摘要

大多数人工智能研究只关注人工智能代理本身,也就是说,在给定输入的情况下,如何改进代理的推理才能产生最佳输出?在我的研究中,我采用了一种新颖的方法,通过使用人工智能来设计人工智能代理的行动环境,从而提高人工智能代理的能力。我的方法可以识别人工智能代理的固有能力和局限性,并找到修改其环境的最佳方法,从而最大限度地提高性能。考虑到这一议程,我在此介绍几个研究项目,这些项目在目标、用于寻找最佳设计的人工智能方法以及它们所对应的现实世界应用方面各不相同。我还将讨论不同的项目如何与我的总体目标相吻合,即利用人工智能促进有效的多机器人协作,并增强机器人和机器与人类的互动方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Better environments for better AI

Most AI research focuses exclusively on the AI agent itself, that is, given some input, what are the improvements to the agent's reasoning that will yield the best possible output? In my research, I take a novel approach to increasing the capabilities of AI agents via the use of AI to design the environments in which they are intended to act. My methods identify the inherent capabilities and limitations of AI agents and find the best way to modify their environment in order to maximize performance. With this agenda in mind, I describe here several research projects that vary in their objective, in the AI methodologies that are applied for finding optimal designs, and in the real-world applications to which they correspond. I also discuss how the different projects fit within my overarching objective of using AI to promote effective multi-agent collaboration and to enhance the way robots and machines interact with humans.

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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
期刊最新文献
Issue Information AI fairness in practice: Paradigm, challenges, and prospects Toward the confident deployment of real-world reinforcement learning agents Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning Efficient and robust sequential decision making algorithms
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