Establishing Meta-Decision-Making for AI: An Ontology of Relevance, Representation and Reasoning
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 2 Oct 2022.
Builds on
5 of 22 references.
References · 5
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
- 2021Morality, Machines and the Interpretation Problem: A Value-based, Wittgensteinian Approach to Building Moral Agentscited by 6
- 2021Understanding and Avoiding AI Failures: A Practical Guidecited by 6
- 2021Have a break from making decisions, have a MARS: The Multi-valued Action Reasoning Systemcited by 6
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.