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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.12581 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | nS4ZZ3h_CXwa |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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