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ARXIV:2605.09780 · AI EXPLAINABILITY · SUBMITTED 12 MAY · 20:16 UTC · FRESHNESS FRESH
ARXIV:2605.09780AI EXPLAINABILITYSUBMITTED 12 MAY · 20:16 UTCFRESHNESS FRESHPaul Kobialka · Andrea Pferscher · Francesco Leofante · Erika Ábrahám · Silvia Lizeth Tapia Tarifa · Einar Broch Johnsen · arXiv
Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents.
Opportunity summary
Pain Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents. So far, these techniques have mainly focused on attributing importance to static input features at a single point in time, and thus…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show how importance scores can be computed by leveraging techniques for strategy synthesis, enabling the efficient computation of these scores despite the non-determinism…
AI Explainability moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
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Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents.
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10.48550/arXiv.2605.09780Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents.
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 characterization of what attributions should represent in MDPs, focusing on explanations that assign importance scores to both individual states and execution paths. We show how importance scores can be computed by leveraging techniques for strategy synthesis, enabling the efficient computation of these scores despite the non-determinism inherent in an MDP. We evaluate our approach on five case-studies, demonstrating its utility in providing interpretable insights into the logic of sequential decision-making agents.
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PROBLEM
Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents. 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 sequent...
METHOD
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-m...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show how importance scores can be computed by leveraging techniques for strategy synthesis, enabling the efficient computation of these scores despite the non-determinism inherent in an MDP. Code avail...
WHY NOW
AI Explainability moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. We show how importance scores can be computed by leveraging techniques for strategy synthesis, enabling the efficient computation of these scores despite the non-determinism inherent in an MDP. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Explainability moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop attribution-based explanations for Markov Decision Processes to understand sequential decision-making agents.
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AI Explainability
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3.0/10 public viability
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