Abstract
Exact inference in complex probabilistic models often incurs prohibitive computational costs. This challenge is particularly acute for autonomous agents in dynamic environments that require frequent, real-time belief updates. Existing methods are often inefficient for ongoing reasoning, as they re-evaluate the entire model upon any change, failing to exploit that real-world information streams have heterogeneous update rates. To address this, we approach the problem from a reactive, asynchronous, probabilistic reasoning perspective. We first introduce Resin (Reactive Signal Inference), a probabilistic programming language that merges probabilistic logic with reactive programming. Furthermore, to provide efficient and exact semantics for Resin, we propose Reactive Circuits (RCs). Formulated as a meta-structure over Algebraic Circuits and asynchronous data streams, RCs are time-dynamic Directed Acyclic Graphs that autonomously adapt themselves based on the volatility of input signals. In high-fidelity drone swarm simulations, our approach achieves several orders of magnitude of speedup over frequency-agnostic inference. We demonstrate that RCs' structural adaptations successfully capture environmental dynamics, significantly reducing latency and facilitating reactive real-time reasoning. By partitioning computations based on the estimated Frequency of Change in the asynchronous inputs, large inference tasks can be decomposed into individually memoized sub-problems. This ensures that only the specific components of a model affected by new information are re-evaluated, drastically reducing redundant computation in streaming contexts.
Abstract (translated)
在复杂的概率模型中进行精确推理往往会导致高昂的计算成本，这对需要频繁实时更新信念的动态环境中的自主代理来说尤其是一个挑战。现有的方法通常无法高效地处理持续性的推理问题，因为它们会在每次变化时重新评估整个模型，而未能利用现实世界信息流具有不同的更新频率这一特性。为了解决这个问题，我们从一种反应式、异步和概率性推理的角度来解决问题。首先，我们介绍了一种名为Resin（Reactive Signal Inference）的概率编程语言，它将概率逻辑与反应式编程相结合。 为了给Resin提供高效且精确的语义学基础，我们提出了“反应电路”(Reactive Circuits, RCs)这一概念。RCs作为一种代数电路和异步数据流上的元结构，它们是时间动态有向无环图(DAG)，能够根据输入信号的变化率自动调整自己。 在高保真无人机群模拟实验中，我们的方法相比频率不敏感的推理方法实现了几个数量级的速度提升。我们证明了RCs的结构适应性成功地捕捉到了环境动力学变化，显著减少了延迟，并促进了反应式的实时推理。通过基于异步输入的变化频率来划分计算任务，大型推断任务可以被分解成单独存储化（memoized）的小问题。 这样确保了只有模型中受到新信息影响的具体部分会被重新评估，极大地减少了流式处理环境中的冗余计算。
URL
https://arxiv.org/abs/2602.05625
