# Computational-level Analysis of Constraint Compliance for General Intelligence

arXiv: https://arxiv.org/abs/2303.04352 · HTML: https://mlsanity.com/paper/2303.04352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [Building Ethically Bounded AI](https://mlsanity.com/paper/1812.03980/) (2018)

## Built on by (most cited first, 1 of 1)

- [Toward Constraint Compliant Goal Formulation and Planning](https://mlsanity.com/paper/2405.12862/) (2024)

## Abstract (arXiv)

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.