Learning Probabilistic Temporal Logic Specifications For Stochastic Systems

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 suc

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyroy2025learning

Related papers

• Sample Efficient Model-free Reinforcement Learning From LTL Specifications With Optimality Guarantees (2023)0.00
• Sample-efficient Reinforcement Learning With Temporal Logic Objectives: Leveraging The Task Specification To Guide Exploration (2024)0.00
• Directed Exploration In Reinforcement Learning From Linear Temporal Logic (2024)0.00
• Regret-free Reinforcement Learning For LTL Specifications (2024)0.00
• Logical Specifications-guided Dynamic Task Sampling For Reinforcement Learning Agents (2024)2.26
• Joint Learning Of Policy With Unknown Temporal Constraints For Safe Reinforcement Learning (2023)0.00
• A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks (2017)11.58
• On The (in)tractability Of Reinforcement Learning For LTL Objectives (2021)0.00