Attribution-based Explanations for Markov Decision Processes
📰 ArXiv cs.AI
Learn to generate attribution-based explanations for Markov Decision Processes to improve transparency in sequential decision-making settings
Action Steps
- Apply attribution techniques to Markov Decision Processes (MDPs) to explain outcomes
- Use techniques like SHAP or LIME to assign numerical scores to inputs in MDPs
- Configure MDPs to incorporate attribution-based explanations for improved transparency
- Test attribution-based explanations on various MDP scenarios to evaluate effectiveness
- Compare different attribution techniques for MDPs to determine the most suitable approach
Who Needs to Know This
AI researchers and engineers working on decision-making systems can benefit from this technique to provide insights into their models' behavior
Key Insight
💡 Attribution-based explanations can be applied to Markov Decision Processes to provide insights into their behavior
Share This
🤖 Improve transparency in sequential decision-making with attribution-based explanations for Markov Decision Processes! #AI #MDPs
Full Article
Title: Attribution-based Explanations for Markov Decision Processes
Abstract:
arXiv:2605.09780v1 Announce Type: new Abstract: Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus fail to generalize to sequential decision-making settings. This paper fills this gap by introducing techniques to generate attribution-based explanations for Markov Decision Processes (MDPs). We give a formal charact
Abstract:
arXiv:2605.09780v1 Announce Type: new Abstract: Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus fail to generalize to sequential decision-making settings. This paper fills this gap by introducing techniques to generate attribution-based explanations for Markov Decision Processes (MDPs). We give a formal charact
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