Reactive Knowledge Representation and Asynchronous Reasoning

2026-02-05

AI Summary

Exact inference in complex probabilistic models incurs substantial computational overhead in dynamic environments, hindering real-time belief updating. This work proposes a reactive asynchronous probabilistic inference framework that innovatively integrates probabilistic logic with reactive programming. Central to this approach is the novel Reactive Circuits (RCs) architecture, which constructs temporally dynamic directed acyclic graphs and selectively recomputes only those subgraphs affected by new observations. By adaptively partitioning the computation graph based on input signal change frequencies and incorporating memoization, the method drastically reduces redundant computations. Evaluated in high-fidelity multi-drone simulations, the framework achieves speedups of several orders of magnitude over frequency-agnostic baselines, significantly lowering inference latency while effectively capturing environmental dynamics.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Probabilistic Inference
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• probabilistic reasoning
• real-time inference
• asynchronous updates
• computational efficiency
• dynamic environments

Innovation

• Reactive Circuits
• Asynchronous Reasoning
• Probabilistic Programming
• Frequency-Aware Inference
• Memoized Sub-problems