Summary of The Role Of Higher-order Cognitive Models in Active Learning, by Oskar Keurulainen et al.
The Role of Higher-Order Cognitive Models in Active Learning
by Oskar Keurulainen, Gokhan Alcan, Ville Kyrki
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 This research paper explores the development of machines that can efficiently collaborate with humans in uncertain environments. The key challenge lies in modeling each other’s behavior and inferring underlying goals, beliefs, or intentions, potentially involving recursive processes. To achieve optimal cooperation, the authors propose a new paradigm for active learning that leverages human feedback while accounting for their higher levels of agency. This approach is demonstrated through a practical example using a higher-order cognitive model, accompanied by a computational study showcasing unique behaviors produced by this model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, scientists want to create machines that can work well with humans in unpredictable situations. To do this, they need to understand how humans think and behave, which involves complex processes like guessing each other’s goals and intentions. This paper suggests a new way for machines to learn from humans, taking into account the fact that humans have more control over their actions than computers do. A practical example is provided to demonstrate how this approach works. |
Keywords
* Artificial intelligence * Active learning
