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:人类行为受到约束的规范和规则。规则、“礼仪”、法律和道德义务是一些规范行为的例子。这些约束系统是“混乱的”:单个约束通常定义不清,确定在特定情况下哪些约束具有相关性可能是未知或模糊的,约束相互作用和冲突,以及在相关约束范围内行事可能是一个重要的挑战,特别是在需要快速做决策的情况下。尽管存在这样的混乱,人类还是能够快速、可靠地考虑约束因素来做出决策。通用的人工智能代理也必须能够处理实际约束系统的混乱,以便能够预测和可靠地行事。在本文中,我们为通用代理的约束符合性特征化复杂性来源,并描述了一种约束符合性的计算层次分析。我们根据计算级别分析确定了关键算法要求,并概述了约束符合性的通用方法的初步探索性实现。