Human behavior is conditioned by codes and norms that constrain action. Rules, ``manners,'' laws, and moral imperatives are examples of classes of constraints that govern human behavior. These systems of constraints are ``messy:'' individual constraints are often poorly defined, what constraints are relevant in a particular situation may be unknown or ambiguous, constraints interact and conflict with one another, and determining how to act within the bounds of the relevant constraints may be a significant challenge, especially when rapid decisions are needed. Despite such messiness, humans incorporate constraints in their decisions robustly and rapidly. General, artificially-intelligent agents must also be able to navigate the messiness of systems of real-world constraints in order to behave predictability and reliably. In this paper, we characterize sources of complexity in constraint processing for general agents and describe a computational-level analysis for such \textit{constraint compliance}. We identify key algorithmic requirements based on the computational-level analysis and outline an initial, exploratory implementation of a general approach to constraint compliance.

翻译：人类行为受制于约束行动的规范和准则。规则、“礼仪”、法律和道德指令是支配人类行为的约束条件类别示例。这些约束系统是“混乱的”：个体约束条件往往定义模糊，特定情境中相关约束条件可能未知或存在歧义，约束条件之间相互影响并产生冲突，在相关约束范围内确定行动方式可能构成重大挑战，尤其在需要快速决策时。尽管存在这种混乱性，人类仍能稳健快速地在其决策中融入约束条件。通用人工智能体同样需要能够应对真实世界约束系统的混乱性，以实现可预测且可靠的行为。本文描述了通用智能体处理约束条件时复杂性的来源，并对这种“约束遵从性”提出了计算层面的分析。我们基于该计算层面分析识别了关键算法需求，并概述了一种通用约束遵从方法的初步探索性实现方案。