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

2025-06-21

AI Summary

To address the challenge that machine learning (ML) model outputs lack direct interpretability and verifiability for complex, decision-critical workflows, this paper proposes a perception–reasoning closed-loop framework. It models ML probabilistic outputs as truth intervals and integrates them into PyReason—an open-world temporal logic reasoning engine—while unifying generalized annotation logic, temporal reasoning, knowledge graphs, and model polling. The method enables dynamic, heterogeneous aggregation of multi-source ML outputs. Its key innovations include continuous truth-interval updating, minimization of model re-evaluation, and end-to-end traceable reasoning provenance. Evaluated across manufacturing, healthcare, and business domains, the framework significantly enhances transparency, adaptability, and temporal responsiveness of automated decisions. It provides a scalable, knowledge-driven architecture for real-time intelligent decision-making.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Description Logics
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Recent advancements in Machine Learning (ML) have yielded powerful models capable of extracting structured information from diverse and complex data sources. However, a significant challenge lies in translating these perceptual or extractive outputs into actionable, reasoned decisions within complex operational workflows. To address these challenges, this paper introduces a novel approach that integrates the outputs from 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 seamless incorporation of real-valued outputs (e.g., probabilities, confidence scores) from diverse 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, ensuring real-tine adaptive decision-making. Furthermore, its native support for temporal reasoning, knowledge graph integration, and fully explainable interface traces enables sophisticated analysis over 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 finds utility across numerous domains, including manufacturing, healthcare, and business operations.

Problem

• Integrate ML outputs with logic for actionable decisions
• Enable real-time adaptive reasoning using temporal logic
• Combine perception and logic for process automation

Innovation

• Integrates ML models with PyReason logic engine
• Converts ML outputs into logical facts dynamically
• Supports temporal reasoning and explainable interfaces