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 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与准则。规则、"礼仪"、法律和道德律令是约束人类行为的一类典型系统。这类约束系统具有"混杂性"：单个约束常定义模糊，特定情境下相关约束可能未知或含混不清，约束之间相互关联、彼此冲突，而在相关约束范围内确定行动方式极具挑战，尤其在需要快速决策时。尽管存在这种混杂性，人类仍能稳健而迅速地将其融入决策过程。通用人工智能体若想实现可预测且可靠的行为，同样必须能够应对真实世界约束系统的混杂性。本文针对通用智能体约束处理的复杂性来源进行表征，并提出此类约束遵守的计算层面分析框架。基于该分析框架，我们识别出关键算法需求，并初步勾勒出一种通用约束遵守方法的探索性实现方案。