Summary of Counterfactual Influence in Markov Decision Processes, by Milad Kazemi et al.
Counterfactual Influence in Markov Decision Processes
by Milad Kazemi, Jessica Lally, Ekaterina Tishchenko, Hana Chockler, Nicola Paoletti
First submitted to arxiv on: 13 Feb 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 This paper tackles a crucial issue in counterfactual inference for Markov Decision Processes (MDPs), allowing us to derive what-if scenarios describing alternative paths obtained by different action sequences. However, as the hypothetical states and actions diverge from the observed ones, the analysis may no longer be tailored to the individual observation, resulting in interventional outcomes rather than counterfactual ones. The authors introduce a formal characterization of influence based on comparing counterfactual and interventional distributions, devise an algorithm for constructing counterfactual models that satisfy influence constraints, and derive optimal policies while staying under the observed path’s influence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand what would have happened if things had gone differently in certain situations. Imagine you’re trying to decide which road to take on a trip – this research is about figuring out what would have happened if you’d taken a different route. The problem is that sometimes the predictions get too far from the real situation, making them less helpful. To solve this, the researchers develop new methods for understanding how one event can affect another. They test these methods and show that they can find good solutions while still considering what’s happening in the real world. |
Keywords
* Artificial intelligence * Inference
