Ensuring Logic in the Fog: Sound POMDP Synthesis with LTL Objectives
The paper discusses a novel approach to synthesizing autonomous agents that can operate in uncertain environments while following complex temporal constraints. It introduces a sound reward-shaping mechanism that generates belief-dependent rewards based on Linear Temporal Logic (LTL) satisfaction. The proposed method enhances Monte Carlo Planning, allowing agents to effectively navigate partial observability and demonstrating scalability across various benchmark domains.
- ▪The paper addresses the challenge of synthesizing autonomous agents in uncertain environments.
- ▪It presents a sound reward-shaping mechanism that relies on LTL satisfaction.
- ▪The approach integrates with Monte Carlo Planning to improve agent navigation in partially observable settings.
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Computer Science > Logic in Computer Science arXiv:2605.12581 (cs) [Submitted on 12 May 2026] Title:Ensuring Logic in the Fog: Sound POMDP Synthesis with LTL Objectives Authors:Can Zhou, Yulong Gao, Pian Yu View a PDF of the paper titled Ensuring Logic in the Fog: Sound POMDP Synthesis with LTL Objectives, by Can Zhou and 2 other authors View PDF HTML (experimental) Abstract:Synthesising autonomous agents that can navigate uncertain environments while adhering to complex temporal constraints remains a fundamental challenge.
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