Learning probabilistic temporal logic specifications for stochastic systems

Conference item

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.

Authors: Roy, R; Pote, Y; Parker, D; Kwiatkowska, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: International Joint Conferences on Artificial Intelligence Organization
• Host title: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
• Pages: 4642-4650
• Publication date: 2025-09-19
• Acceptance date: 2025-04-29
• Event title: 34th International Joint Conference on Artificial Intelligence (IJCAI 2025)
• Event location: Montreal
• Event website: https://2025.ijcai.org/
• Event start date: 2025-08-16
• Event end date: 2025-08-22
• DOI: 10.24963/ijcai.2025/517
• EISBN: 9781956792065
• Language: English
• Keywords: knowledge representation languages; planning under uncertainty; uncertainty representations