Summary of Expectation Alignment: Handling Reward Misspecification in the Presence Of Expectation Mismatch, by Malek Mechergui et al.
Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch
by Malek Mechergui, Sarath Sreedharan
First submitted to arxiv on: 12 Apr 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 The paper addresses the critical challenge of detecting and handling misspecified objectives in Artificial Intelligence (AI) safety research, particularly in reward functions. It presents an Expectation Alignment (EAL) framework that provides a formal explanatory structure for understanding objective misspecification and its causes. The EAL framework not only offers insights into existing methods’ limitations but also proposes novel solution strategies. The authors develop an interactive algorithm that infers potential user expectations about system behavior using the specified reward, mapping this inference problem to linear programs. This algorithm is evaluated on standard Markov Decision Process (MDP) benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a big problem in artificial intelligence called “misspecified objectives.” This means when the goal or purpose of an AI system is not what humans intended it to be. The researchers created a new framework called Expectation Alignment that helps understand why this happens and how to fix it. They also came up with a new algorithm that can figure out what people want an AI system to do, based on the rules they set for it. This algorithm was tested using common examples. |
Keywords
* Artificial intelligence * Alignment * Inference
