Computational-level Analysis of Constraint Compliance for General Intelligence
Abstract
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.
Cite
@article{arxiv.2303.04352,
title = {Computational-level Analysis of Constraint Compliance for General Intelligence},
author = {Robert E. Wray and Steven J. Jones and John E. Laird},
journal= {arXiv preprint arXiv:2303.04352},
year = {2023}
}
Comments
10 pages, 2 figures. Accepted for presentation at AGI 2023. Corrected author list (segmented list) and abstract text artifacts