Machine Learning Model Integration with Open World Temporal Logic for Process Automation

TL;DR

This paper presents a novel system that integrates machine learning outputs with a temporal logic reasoning framework to enable real-time, explainable decision-making in complex operational workflows across various domains.

Contribution

It introduces a method to incorporate ML model outputs into an open-world temporal logic system, enabling dynamic, explainable, and real-time process automation.

Findings

Supports real-time polling and conversion of ML outputs into logical facts.

Enables dynamic recomputation of minimal models for decision-making.

Provides explainable traces for process analysis and knowledge integration.

Abstract

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.