Summary of Active Inference As a Model Of Agency, by Lancelot Da Costa et al.
Active Inference as a Model of Agency
by Lancelot Da Costa, Samuel Tenka, Dominic Zhao, Noor Sajid
First submitted to arxiv on: 23 Jan 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 The paper introduces a new perspective on agency, moving beyond reward maximization. It presents a framework called active inference, which integrates exploration and exploitation by minimizing risk and ambiguity about states of the world. This normative Bayesian approach simulates biological agency, widely used in neuroscience, reinforcement learning (RL), and robotics. The framework provides a principled solution to the exploration-exploitation dilemma, offering an explainable recipe for simulating behavior under a generative world model. It can be used as a tool to uncover and compare the commitments and assumptions of more specific models of agency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we think about being “in charge” or having control over our actions. Instead of just trying to get rewards, it says that we should think about minimizing uncertainty and risk. This idea is called active inference and it’s like a recipe for how we make decisions. It’s used in things like studying animal brains, making robots work better, and even figuring out how humans learn. The big idea is that this way of thinking can help us understand why different animals or machines behave differently. |
Keywords
* Artificial intelligence * Inference * Reinforcement learning
