(A)Life as It Could Be.

IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Life Pub Date : 2024-10-11 DOI:10.1162/artl_a_00455
Randall D Beer
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Abstract

On this 30th anniversary of the founding of the Artificial Life journal, I share some personal reflections on my own history of engagement with the field, my own particular assessment of its current status, and my vision for its future development. At the very least, I hope to stimulate some necessary critical conversations about the field of Artificial Life and where it is going.

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(A)可能的生活。
值此《人工生命》杂志创刊 30 周年之际,我将与大家分享一些个人感想,包括我参与该领域的历史、我对该领域现状的具体评估以及我对该领域未来发展的愿景。至少,我希望能激发人们对人工生命领域及其发展方向进行一些必要的批判性对话。
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来源期刊
Artificial Life
Artificial Life 工程技术-计算机:理论方法
CiteScore
4.70
自引率
7.70%
发文量
38
审稿时长
>12 weeks
期刊介绍: Artificial Life, launched in the fall of 1993, has become the unifying forum for the exchange of scientific information on the study of artificial systems that exhibit the behavioral characteristics of natural living systems, through the synthesis or simulation using computational (software), robotic (hardware), and/or physicochemical (wetware) means. Each issue features cutting-edge research on artificial life that advances the state-of-the-art of our knowledge about various aspects of living systems such as: Artificial chemistry and the origins of life Self-assembly, growth, and development Self-replication and self-repair Systems and synthetic biology Perception, cognition, and behavior Embodiment and enactivism Collective behaviors of swarms Evolutionary and ecological dynamics Open-endedness and creativity Social organization and cultural evolution Societal and technological implications Philosophy and aesthetics Applications to biology, medicine, business, education, or entertainment.
期刊最新文献
Complexity, Artificial Life, and Artificial Intelligence. Neurons as Autoencoders. Evolvability in Artificial Development of Large, Complex Structures and the Principle of Terminal Addition. Investigating the Limits of Familiarity-Based Navigation. Network Bottlenecks and Task Structure Control the Evolution of Interpretable Learning Rules in a Foraging Agent.
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