Establishing Meta-Decision-Making for AI: An Ontology of Relevance, Representation and Reasoning
This foundational work addresses the need for structured decision-making frameworks in AI, particularly for safety and risk mitigation in cognitive systems, but it is incremental as it builds on existing literature without introducing new methods or data.
The paper tackles the problem of establishing meta-decision-making for AI by proposing an ontology based on relevance, representation, and reasoning, aiming to improve autonomy and provide a framework for metrics and benchmarks.
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.