提出新方法高效解决不确定环境下传感器选择难题。
Scaling Observation-aware Planning in Uncertain Domains
- 通过分解POMDP模型，自动设计合理的观测策略。
- 在实例规模和运行时间上分别提升300倍和5万倍。
- 适合机器人感知系统设计与资源受限场景的开发者。
在不确定环境中决定为智能体部署哪些感知能力是一项基础性工程挑战，需权衡任务完成度与硬件及计算成本。该问题曾被形式化为最优可观测性问题（OOP），基于部分可观察马尔可夫决策过程（POMDP）模型。本文研究（子）符号技术，以扩展求解可判定的OOP片段——传感器选择问题（SSP）与位置可观测性问题（POP）。除改进基于参数合成的原有方法外，还提出一种新求解方法：通过分解POMDP识别合理观测函数，在实例规模和运行时间上分别提升3和5个数量级。
原文摘要 · Abstract (English)
Deciding which sensing capabilities to deploy on an agent in uncertain domains is a fundamental engineering challenge, in which one balances task achievability against the high costs of hardware and processing. This problem has previously been formalized as the Optimal Observability Problem (OOP), based on the well-known Partially Observable Markov Decision Process (POMDP) model for decision-making. This work studies (sub-)symbolic techniques to scale solving of decidable fragments of the OOP, namely the Sensor Selection Problem (SSP) and the Positional Observability Problem (POP). Besides improving the original approach based on parameter synthesis, we develop a new solving method that identifies sensible observation functions via decomposition of POMDPs, improving performance by 3 and 5 orders of magnitude for instance size and runtime, respectively.
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