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

arXiv: https://arxiv.org/abs/2506.17776 · HTML: https://mlsanity.com/paper/2506.17776/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [Semantic Probabilistic Layers for Neuro-Symbolic Learning](https://mlsanity.com/paper/2206.00426/) (2022)
- [NeurASP: Embracing Neural Networks into Answer Set Programming](https://mlsanity.com/paper/2307.07700/) (2023)
- [PyReason: Software for Open World Temporal Logic](https://mlsanity.com/paper/2302.13482/) (2023)
- [Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification](https://mlsanity.com/paper/2308.14250/) (2023)
- [Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge](https://mlsanity.com/paper/2407.15192/) (2024)
- [Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments](https://mlsanity.com/paper/2505.19361/) (2025)

## Built on by (most cited first, 1 of 1)

- [Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning](https://mlsanity.com/paper/2509.02958/) (2025)

## Abstract (arXiv)

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.