代理性取決於框架。

Agency Is Frame-Dependent

February 6, 2025

作者: David Abel, André Barreto, Michael Bowling, Will Dabney, Shi Dong, Steven Hansen, Anna Harutyunyan, Khimya Khetarpal, Clare Lyle, Razvan Pascanu, Georgios Piliouras, Doina Precup, Jonathan Richens, Mark Rowland, Tom Schaul, Satinder Singh

cs.AI

摘要

代理性是系統將結果引導至目標的能力，是生物學、哲學、認知科學和人工智慧研究的核心主題。確定系統是否具有代理性是一個極具挑戰性的問題：例如，Dennett（1989）強調了確定岩石、恆溫器或機器人是否具有代理性的難題。我們從強化學習的觀點來探討這個謎題，主張代理性從根本上是依賴於框架的：對系統代理性的任何測量都必須相對於參考框架進行。我們通過提出一個哲學論點來支持這一主張，該論點指出Barandiaran等人（2009）和Moreno（2018）提出的代理性基本特性本身是依賴於框架的。我們得出結論，任何有關代理性的基礎科學都需要框架依賴性，並討論這一主張對強化學習的影響。

English

Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.