将系统解释为求解POMDPs：迈向对智能体形式化理解的一步
Interpreting systems as solving POMDPs: a step towards a formal understanding of agency
AI总结：
本文基于部分可观察马尔可夫过程（POMDPs）理论，提出将系统解释为同时具备信念、目标和行动的POMDP解，从而为“系统成为智能体”提供更一般的形式化定义。
摘要：
在何种情况下可以说一个系统具有信念和目标，并且此类与智能体相关的特征如何与其物理状态相关联？最近的研究提出了一种解释映射的概念，这是一种将系统状态映射到概率分布的函数，该概率分布代表其对外部世界的信念。这种映射并非完全任意，因为它归因于系统的信念必须以符合贝叶斯定理的方式随时间演化，因此系统的动态特性约束了其可能的解释。在此我们基于这一方法，提出一种不仅涉及信念，还涉及目标和行动的解释概念。为此，我们利用了现有的部分可观察马尔可夫过程（POMDPs）理论：我们说一个系统可以被解释为POMDP的解，如果它不仅允许一个描述其对POMDP隐藏状态信念的解释映射，而且采取根据其信念状态为最优的行动。那么，智能体就是一个系统连同对该系统作为POMDP解的解释。尽管POMDPs并不是对“拥有目标”含义的唯一可能表述，但这仍然是迈向系统成为智能体更一般形式化定义的一步。
英文摘要：
Under what circumstances can a system be said to have beliefs and goals, and how do such agency-related features relate to its physical state? Recent work has proposed a notion of interpretation map, a function that maps the state of a system to a probability distribution representing its beliefs about an external world. Such a map is not completely arbitrary, as the beliefs it attributes to the system must evolve over time in a manner that is consistent with Bayes' theorem, and consequently the dynamics of a system constrain its possible interpretations. Here we build on this approach, proposing a notion of interpretation not just in terms of beliefs but in terms of goals and actions. To do this we make use of the existing theory of partially observable Markov processes (POMDPs): we say that a system can be interpreted as a solution to a POMDP if it not only admits an interpretation map describing its beliefs about the hidden state of a POMDP but also takes actions that are optimal according to its belief state. An agent is then a system together with an interpretation of this system as a POMDP solution. Although POMDPs are not the only possible formulation of what it means to have a goal, this nevertheless represents a step towards a more general formal definition of what it means for a system to be an agent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
