Semantic AI Reasoning With Explainability for Production Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturing facilities rely on non-flexible, hardcoded production routines and lack of transparency in AI-based control mechanisms, making it difficult for expert users to trust and interact with AI systems effectively.
Innovation Solution
A semantic-based mechanism using explainable artificial intelligence (XAI) models that provide explanations for optimization options, allowing seamless user interaction and continuous learning from feedback, enhancing machine-human cooperation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI techniques such as machine learning or deep learning are implemented to improve flexibility and adaptability, then adaptability is improved, but transparency and understandability deteriorate
Solution Approach 1:
The patent introduces an explanation generation module as an intermediary between the AI model and the user. This module translates the internal workings of the black-box AI system into human-understandable explanations, maintaining adaptability while restoring transparency through a mediating layer that bridges the gap between complex AI processing and human comprehension
Solution Approach 2:
The system segments the AI decision-making process into distinct components: the core AI model for adaptability and the explanation generation module for transparency. By separating these functions, the patent allows the AI to maintain its flexible adaptability while the explanation module provides transparent, interpretable outputs without compromising the core AI's performance
2Productivity
If black-box machine learning algorithms are used to improve productivity and automation, then productivity is improved, but ease of operation deteriorates
Solution Approach 1:
The explanation generation module serves as an intermediary that makes black-box AI systems easier to operate by providing human-understandable rationales for AI decisions. This allows expert users to interact with and trust the automated system without sacrificing productivity, as the explanations bridge the gap between automated performance and human comprehension
Solution Approach 2:
The system implements feedback loops where user interactions with explanations and corrections are used to continuously improve the AI model. This feedback mechanism enhances ease of operation by allowing users to effectively guide and refine the AI system while maintaining high productivity through automated processing
3Reliability
If hardcoded, non-flexible production routines are used to ensure reliability and stability, then reliability is improved, but adaptability deteriorates
Solution Approach 1:
The patent transitions from static, hardcoded routines to dynamic AI-based control that can adapt to changing conditions. The AI model learns from data and continuously adjusts its behavior, providing reliability through consistent performance while gaining adaptability through its ability to respond to new situations without requiring manual reprogramming
4Extent of automation
If AI-based control mechanisms are implemented to improve automation, then extent of automation is improved, but ease of operation deteriorates
Solution Approach 1:
The explanation generation module acts as an intermediary that maintains ease of operation while increasing automation. By providing transparent explanations for automated decisions, it allows users to effectively oversee and interact with highly automated systems without being overwhelmed by complexity, thus preserving ease of operation at high levels of automation
Data Source
Figure 1
Figure 2~4
AI summary
The invention relates to a system (1) for feedback-improved automatic solving of production facility related tasks, comprising: an input interface (2) being adapted to receive production facility related data; a semantic data enhancement module (3) being adapted to generate semantically enhanced data based on the production facility related data; a semantic-based reasoning module (4) being adapted to automatically provide an explainable artificial intelligence model, using the semantically enhanced data, wherein the artificial intelligence model relates to a predetermined production facility related task; a user interaction interface (5) being adapted to output explanation data regarding the explainable artificial intelligence model to a user (9); and a feedback module (6) adapted to receive feedback from the user (9) in response to the outputted explanation data and adapted to adjust the semantic-based reasoning module (4) and/or the user interaction interface (5) based on the received feedback.