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：人类行为受到制约行动的准则和规范的约束。规则、"礼仪"、法律和道德律令是支配人类行为的多类约束的例子。这些约束系统是"混乱的"：个体约束往往定义不清，特定情境中哪些约束相关可能未知或模糊不清，约束之间相互影响并发生冲突，且在相关约束范围内确定如何行动可能是一项重大挑战，尤其是在需要快速决策时。尽管存在这种混乱性，人类仍能稳健且快速地在其决策中纳入约束。通用的人工智能体也必须能够驾驭现实世界约束系统的混乱性，以具备可预测性和可靠性。在本文中，我们描述了通用智能体处理约束时复杂性的来源，并对这种"约束遵守"进行了计算层次分析。我们基于计算层次分析确定了关键算法需求，并概述了一种通用约束遵守方法的初步探索性实现。