Evgeny Kusmenko

Model-Driven Development Methodology and Domain-Specific Languages for the Design of Artificial Intelligence in Cyber-Physical Systems


Front	Back

ISBN:

978-3-8440-8286-9

Series:

Aachener Informatik-Berichte, Software Engineering
Herausgeber: Prof. Dr. rer. nat. Bernhard Rumpe
Aachen

Volume:

Keywords:

MDSE; DSLs; Machine Learning; AI

Type of publication:

Thesis

Language:

English

Pages:

342 pages

Figures:

101 figures

Weight:

510 g

Format:

24 x 17 cm

Binding:

Paperback

Price:

39,80 € / 49,80 SFr

Published:

November 2021

Buy:

Download:

Available PDF-Files for this title:

You need the Adobe Reader, to open the files. Here you get help and information, for the download.

These files are not printable.


	Document		Document
	Type		PDF
	Costs		29,85 EUR
	Action		Purchase in obligation and display of file - 2,7 MB (2857477 Byte)
	Action		Purchase in obligation and download of file - 2,7 MB (2857477 Byte)


	Document		Table of contents
	Type		PDF
	Costs		free
	Action		Display of file - 222 kB (227512 Byte)
	Action		Download of file - 222 kB (227512 Byte)

User settings for registered users

You can change your address here or download your paid documents again.

User:	Not logged in.
Actions:	Login / Register Forgotten your password?

Recommendation:

You want to recommend this title?

Review copy:

Here you can order a review copy.

Link:

You want to link this page? Click here.

Export citations:

Text
BibTex
RIS

Abstract:

The development of cyber-physical systems poses a multitude of challenges requiring experts from different fields. Such systems cannot be developed successfully without the support of appropriate processes, languages, and tools. Model-driven software engineering is an important approach which helps development teams to cope with the increasing complexity of today's cyber-physical systems.

The aim of this thesis is to develop a model-driven engineering methodology with a particular focus on interconnected intelligent cyber-physical systems such as cooperative vehicles.
The basis of the proposed methodology is a component-and-connector architecture description language. It features a strong, math-oriented type system abstracting from the technical realization and incorporating physical units. To facilitate the development of self-adaptive systems, the language enables its users to model component and connector arrays and supports architectural runtime reconfiguration. Architectural elements can be altered, added, and removed dynamically upon the occurrence of trigger events.

Furthermore, the proposed methodology provides means for behavior specification and its seamless integration into the software architecture. A matrix-oriented scripting language enables the developer to specify algorithms using a syntax close to the mathematical domain. A dedicated deep learning modeling language is provided for the development and training of neural networks as directed acyclic graphs of neuron layers. The framework supports different learning methods including supervised, reinforcement, and generative adversarial learning, covering a broad range of applications from image and natural language processing to decision making and test data generation.

» more titles from Evgeny Kusmenko