Summary of Measuring Goal-directedness, by Matt Macdermott et al.
Measuring Goal-Directedness
by Matt MacDermott, James Fox, Francesco Belardinelli, Tom Everitt
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 We introduce maximum entropy goal-directedness (MEG), a novel measure of goal-directedness in causal models and Markov decision processes, designed to assess AI’s ability to achieve goals. Our formal framework adapts the maximum causal entropy approach used in inverse reinforcement learning. MEG can evaluate goal-directedness with respect to known utility functions, hypotheses, or random variables. We demonstrate our algorithms through small-scale experiments, showcasing its effectiveness. By quantifying goal-directedness, MEG addresses concerns about AI harm and contributes to philosophical discussions on agency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a machine that can achieve goals. To understand how well it does this, we need a way to measure its ability to reach those goals. We created a new method called maximum entropy goal-directedness (MEG) to do just that. MEG helps us figure out if AI is good at reaching its goals and whether it might cause harm. This is important because AI is becoming more and more powerful, so we need ways to understand how it works. Our method can be used in many different situations, from simple games to complex real-world problems. |
Keywords
» Artificial intelligence » Reinforcement learning
