# Establishing Meta-Decision-Making for AI: An Ontology of Relevance, Representation and Reasoning

arXiv: https://arxiv.org/abs/2210.00608 · HTML: https://mlsanity.com/paper/2210.00608/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Morality, Machines and the Interpretation Problem: A Value-based, Wittgensteinian Approach to Building Moral Agents](https://mlsanity.com/paper/2103.02728/) (2021)
- [Understanding and Avoiding AI Failures: A Practical Guide](https://mlsanity.com/paper/2104.12582/) (2021)
- [Have a break from making decisions, have a MARS: The Multi-valued Action Reasoning System](https://mlsanity.com/paper/2109.03283/) (2021)

## Built on by (most cited first, 2 of 2)

- [If our aim is to build morality into an artificial agent, how might we begin to go about doing so?](https://mlsanity.com/paper/2310.08295/) (2023)
- [Can a Machine be Conscious? Towards Universal Criteria for Machine Consciousness](https://mlsanity.com/paper/2404.15369/) (2024)

## Abstract (arXiv)

We propose an ontology of building decision-making systems, with the aim of establishing Meta-Decision-Making for Artificial Intelligence (AI), improving autonomy, and creating a framework to build metrics and benchmarks upon. To this end, we propose the three parts of Relevance, Representation, and Reasoning, and discuss their value in ensuring safety and mitigating risk in the context of third wave cognitive systems. Our nomenclature reflects the literature on decision-making, and our ontology allows researchers that adopt it to frame their work in relation to one or more of these parts.