Proceedings

Learning Probabilistic Temporal Logic Specifications for Stochastic Systems

Rajarshi Roy, Yash Pote, Dave Parker, Marta Kwiatkowska

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4642-4650.

https://doi.org/10.24963/ijcai.2025/517

Keywords

  • Uncertainty in AI: UAI: Uncertainty representations
  • Knowledge Representation and Reasoning: KRR: Knowledge representation languages
  • Planning and Scheduling: PS: Planning under uncertainty

Abstract

There has been substantial progress in the inference of formal behavioural specifications from sample trajectories, for example using Linear Temporal Logic (LTL). However, these techniques cannot handle specifications that correctly characterise systems with stochastic behaviour, which occur commonly in reinforcement learning and formal verification. We consider the passive learning problem of inferring a Boolean combination of probabilistic LTL (PLTL) formulas from a set of Markov chains, classified as either positive or negative. We propose a novel learning algorithm that infers concise PLTL specifications, leveraging grammar-based enumeration, search heuristics, probabilistic model checking and Boolean set-cover procedures. We demonstrate the effectiveness of our algorithm in two use cases: learning from policies induced by RL algorithms and learning from variants of a probabilistic model. In both cases, our method automatically and efficiently extracts PLTL specifications that succinctly characterize the temporal differences between the policies or model variants.

Rajarshi Roy, Yash Pote, Dave Parker, and Marta Kwiatkowska. 2025. Learning Probabilistic Temporal Logic Specifications for Stochastic Systems. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4642–4650. https://doi.org/10.24963/ijcai.2025/517
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