Summary of The Reasons That Agents Act: Intention and Instrumental Goals, by Francis Rhys Ward and Matt Macdermott and Francesco Belardinelli and Francesca Toni and Tom Everitt
The Reasons that Agents Act: Intention and Instrumental Goals
by Francis Rhys Ward, Matt MacDermott, Francesco Belardinelli, Francesca Toni, Tom Everitt
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning has been grappling with the concept of intentionality, which underlies agency, manipulation, legal responsibility, and blame. Despite its importance, there is no universally accepted theory of intention applicable to AI systems. This paper operationalizes intention by focusing on the reasons behind an agent’s decision-making process. A formal definition of intention is introduced in structural causal influence models, grounded in philosophical concepts and applicable to real-world machine learning systems. The authors demonstrate that this definition captures the intuitive notion of intent and satisfies desiderata set out by past work. Additionally, they show how their definition relates to actual causality, instrumental goals, and safe AI agents. Finally, the paper demonstrates how this definition can be used to infer the intentions of reinforcement learning agents and language models from their behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems are trying to understand what we mean by “intention”. It’s a big deal because it affects many other important things like who gets blamed when something goes wrong. Right now, there is no one way that scientists agree on how to define intention for AI. This paper tries to solve this problem by explaining why an AI does something and using that information to figure out what its intentions are. They also show how their definition relates to other important ideas in the field. Finally, they use their definition to understand what language models and AI agents really want when they do things. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
