Summary of Adaptive Alignment: Dynamic Preference Adjustments Via Multi-objective Reinforcement Learning For Pluralistic Ai, by Hadassah Harland et al.
Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI
by Hadassah Harland, Richard Dazeley, Peter Vamplew, Hashini Senaratne, Bahareh Nakisa, Francisco Cruz
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed framework in this paper introduces a dynamic approach to aligning artificial intelligence (AI) with diverse human needs and values using Multi Objective Reinforcement Learning (MORL). The authors aim to address the challenge of designing and deploying AI that is in accordance with shifting user preferences. By adopting a post-learning policy selection adjustment, the MORL approach enables AI systems to adapt to changing user needs and values. The paper outlines the technical details of the implementation and examines the broader implications of adopting this retroactive alignment approach through a sociotechnical systems perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create AI that works with people who have different opinions and values. To do this, the authors use a special type of learning called Multi Objective Reinforcement Learning (MORL). MORL helps AI systems adjust to changing user preferences by making decisions after they’ve learned from experience. This approach is important because it allows AI to work better with people who have different ideas about what’s important. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning
