Knowledge Driven Synthesis Using Resource-Capability Semantics for Control Software Design

Abstract

This paper presents a knowledge-driven approach for automated synthesis of controllers in three different use-cases. The approach addresses the engineering challenge posed by Industrie 4.0, which requires fast, reliable, and flexible integration of multiple heterogeneous hardware and software components. Manual design approaches are not scalable for large systems due to their complexity. The proposed approach captures resource-capability knowledge and uses a reasoning-based synthesis mechanism to compose a controller design for a plant goal. The approach uses domain-specific languages (DSLs) to describe the components, their interfaces, and capabilities. The generated control designs are executable codes that implement the control strategy. The proposed approach reduces the average engineering time by 70% and generates on an average 60% of the executable code in each use-case. The approach uses a knowledge repository to store resource-capability knowledge and enables rapid prototyping and iterative design. The proposed approach provides a promising solution to automate the synthesis of controllers in different use-cases with multiple heterogeneous hardware and software components satisfaction.INDEX TERMS Automated software development, control software, capability knowledge model, knowledge driven approach, semantic ontology.

Enhancing Agricultural Knowledge Management using an efficient & Novel Ontology-Based Approach Leveraging BERT-GPT and Graph Recurrent Q Learning Network

“…Ontologies, which are formal representations of domain-specific knowledge, have emerged as a valuable tool in organizing and representing the vast knowledge resources in agriculture. They enable the seamless integration of diverse data sources, facilitating efficient information retrieval and analysis [1]- [3] via use of Knowledge Graphs (KGs). graphs, respectively.…”