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arXiv · 2303.04352

Computational-level Analysis of Constraint Compliance for General Intelligence

Robert E. Wray·Steven J. Jones·John E. Laird
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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.

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Robert E. Wray, Steven J. Jones, John E. Laird. 2023-03-08. Computational-level Analysis of Constraint Compliance for General Intelligence. https://arxiv.org/abs/2303.04352

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