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.

本文探讨了人类行为的约束条件、现实情境中这些限制的复杂性和智能代理如何适应这些约束条件，旨在给出一种通用的约束条件处理方法。