REBA: A Revealed Belief Automaton Framework for Online Planning in Continuous POMDPs
Online planning in continuous partially observable Markov decision processes (POMDPs) using $ω$-regular specifications requires handling continuous belief dynamics within the finite symbolic memory in order to track temporal progress. Existing methods based on either direct search in belief space or predefined discrete abstractions suffer from drawbacks, e.g., lack of symbolic memory for long-horizon logical progress or difficult to certify from noisy online beliefs. As such, obtaining reliable symbolic states online from continuous observations remains a challenge. To address this issue, we introduce the Revealed Belief Automaton (REBA), an event-driven framework that advances the resear...
| Authors | Xiangwei Chen, Lingling Fang, Andreas Holzinger et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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