Understanding Social Robots: Attribution of Intentional Agency to Artificial and Biological Bodies

IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Life Pub Date : 2023-08-01 DOI:10.1162/artl_a_00404
Tom Ziemke
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引用次数: 1

Abstract

Much research in robotic artificial intelligence (AI) and Artificial Life has focused on autonomous agents as an embodied and situated approach to AI. Such systems are commonly viewed as overcoming many of the philosophical problems associated with traditional computationalist AI and cognitive science, such as the grounding problem (Harnad) or the lack of intentionality (Searle), because they have the physical and sensorimotor grounding that traditional AI was argued to lack. Robot lawn mowers and self-driving cars, for example, more or less reliably avoid obstacles, approach charging stations, and so on—and therefore might be considered to have some form of artificial intentionality or intentional directedness. It should be noted, though, that the fact that robots share physical environments with people does not necessarily mean that they are situated in the same perceptual and social world as humans. For people encountering socially interactive systems, such as social robots or automated vehicles, this poses the nontrivial challenge to interpret them as intentional agents to understand and anticipate their behavior but also to keep in mind that the intentionality of artificial bodies is fundamentally different from their natural counterparts. This requires, on one hand, a “suspension of disbelief ” but, on the other hand, also a capacity for the “suspension of belief.” This dual nature of (attributed) artificial intentionality has been addressed only rather superficially in embodied AI and social robotics research. It is therefore argued that Bourgine and Varela’s notion of Artificial Life as the practice of autonomous systems needs to be complemented with a practice of socially interactive autonomous systems, guided by a better understanding of the differences between artificial and biological bodies and their implications in the context of social interactions between people and technology.
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理解社交机器人:人工和生物身体的意向代理归因
机器人人工智能(AI)和人工生命的许多研究都集中在自主代理上,将其作为人工智能的具体化和定位方法。这种系统通常被视为克服了许多与传统计算主义人工智能和认知科学相关的哲学问题,例如基础问题(Harnad)或缺乏意向性(Searle),因为它们具有传统人工智能被认为缺乏的物理和感觉运动基础。例如,机器人割草机和自动驾驶汽车或多或少能可靠地避开障碍物、接近充电站等,因此可能被认为具有某种形式的人工意向性或有意定向。值得注意的是,机器人与人类共享物理环境这一事实并不一定意味着它们与人类处于相同的感知和社会世界。对于遇到社交互动系统(如社交机器人或自动驾驶汽车)的人来说,这提出了一个重要的挑战,即将它们解释为理解和预测其行为的有意代理,但同时也要记住,人造身体的意向性与它们的自然对应物有着根本的不同。这一方面需要“暂停怀疑”,但另一方面也需要“暂停信仰”的能力。这种(归因于的)人工意向性的双重性质在具体的人工智能和社会机器人研究中只得到了相当肤浅的解决。因此,有人认为,布尔金和瓦雷拉的人工生命概念作为自主系统的实践需要与社会互动自主系统的实践相辅相成,以更好地理解人工和生物身体之间的差异及其在人与技术之间的社会互动背景下的含义为指导。
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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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