Recent advances in Machine Learning (ML) have produced models that extract structured information from complex data. However, a significant challenge lies in translating these perceptual or extractive outputs into actionable and explainable decisions within complex operational workflows. To address these challenges, this paper introduces a novel approach that integrates the outputs of various machine learning models directly with the PyReason framework, an open-world temporal logic programming reasoning engine. PyReason's foundation in generalized annotated logic allows for the incorporation of real-valued outputs (e.g., probabilities, confidence scores) from a diverse set of ML models, treating them as truth intervals within its logical framework. Crucially, PyReason provides mechanisms, implemented in Python, to continuously poll ML model outputs, convert them into logical facts, and dynamically recompute the minimal model to enable decision-making in real-time. Furthermore, its native support for temporal reasoning, knowledge graph integration, and fully explainable interface traces enables an analysis of time-sensitive process data and existing organizational knowledge. By combining the strengths of perception and extraction from ML models with the logical deduction and transparency of PyReason, we aim to create a powerful system for automating complex processes. This integration is well suited for use cases in numerous domains, including manufacturing, healthcare, and business operations.

翻译：机器学习（ML）的最新进展已催生出能够从复杂数据中提取结构化信息的模型。然而，一个重大挑战在于如何将这些感知或提取性输出转化为复杂操作工作流中可执行且可解释的决策。为应对这些挑战，本文提出一种新颖方法，将多种机器学习模型的输出直接与PyReason框架集成——PyReason是一个开放世界时序逻辑编程推理引擎。PyReason基于广义注释逻辑，允许纳入来自多样化ML模型的实值输出（如概率、置信度分数），并将其视为其逻辑框架内的真值区间。关键在于，PyReason提供了通过Python实现的机制，能够持续轮询ML模型输出、将其转换为逻辑事实，并动态重新计算最小模型以实现实时决策。此外，其原生支持的时序推理、知识图谱集成以及完全可解释的接口追踪功能，使得对时间敏感的流程数据和现有组织知识进行分析成为可能。通过结合ML模型的感知与提取能力，以及PyReason的逻辑推理与透明度，我们旨在构建一个用于自动化复杂流程的强大系统。这种集成非常适用于众多领域的应用场景，包括制造业、医疗保健和商业运营。