Automated Model of Computation Generation via Machine Learning
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Solution Overview
Problem
The challenge lies in maintaining and adapting complex models of computation for cyber-physical systems, such as vehicle computing units, due to frequent specification changes and the manual or low-automation nature of current methods, which is time-consuming, costly, and error-prone.
Innovation Solution
A computer-implemented method using machine learning to automatically generate models of computation, such as pushdown automata, state machines, and Petri nets, based on specifications and prompts, with subsequent evaluation and potential adaptation of the machine learning model for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If models of computation are created and maintained manually by software engineers, then the models can be adapted to specifications, but the process is time-consuming, costly, and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of model creation with an automated machine learning system. The machine learning model automatically generates models of computation from natural language specifications, eliminating the need for manual software engineering efforts while improving both speed and consistency.
Solution Approach 2:
The system enables self-service by allowing specifications written in natural language to automatically generate corresponding models of computation without requiring expert software engineers. The machine learning model serves itself to translate human intent into formal computational models.
2Adaptability or versatility
If models of computation are created manually, then flexibility in handling complex specifications is maintained, but the process becomes costly and difficult to maintain
Solution Approach 1:
The patent replaces complex manual processes with an automated machine learning system that handles specification adaptability. The system accepts natural language inputs and automatically generates appropriate models, making the creation process easier while maintaining flexibility to handle diverse and changing specifications.
Solution Approach 2:
The machine learning model serves multiple functions: it interprets natural language specifications, determines the appropriate type of model of computation needed, generates the model, and can even translate it to executable code. This universal approach handles various specification types without requiring different manual processes.
3Productivity
If automation is increased in model generation, then time and cost are reduced, but reliability may be compromised
Solution Approach 1:
The patent incorporates feedback mechanisms where the generated models can be evaluated and validated. The system can iterate on generated models, refine them based on evaluation results, and ensure they meet the specified requirements, thereby maintaining reliability while achieving automation.
Solution Approach 2:
The system performs preliminary actions by generating multiple candidate models and evaluating them before finalizing the output. This preliminary evaluation and validation process ensures correctness is checked before deployment, maintaining reliability in the automated process.
Data Source
AI summary
A computer-implemented method for the automated generation of a model of computation. The method includes generating, via a machine learning model, at least one model of computation based at least on a specification that the at least one model of computation is to fulfill, and a prompt; and evaluating the at least one model of computation, resulting in an evaluation result. A computer-implemented method for further training a machine learning model, wherein the machine learning model is designed to generate at least one model of computation is also described. The method includes adapting the machine learning model at least based on at least one model of computation and at least one evaluation result, wherein the at least one evaluation result results from evaluating the at least one model of computation.