Reactive Knowledge Representation and Asynchronous Reasoning

TL;DR

This paper introduces Resin, a reactive probabilistic programming language, and Reactive Circuits, a structure for efficient, asynchronous inference that adapts to input volatility, enabling real-time reasoning in dynamic environments.

Contribution

The paper presents Resin and Reactive Circuits, novel frameworks that enable efficient, exact, and reactive probabilistic inference by exploiting asynchronous data streams and input change frequencies.

Findings

Achieves several orders of magnitude speedup in drone swarm simulations

Reactive Circuits adapt to environmental dynamics, reducing latency

Partitioning inference based on change frequency improves efficiency

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.