Making EMF Intelligent with AI

Transcription

1 Interpreting the world on a different plane Making EMF Intelligent with AI Robert Bosch Engineering and Business Solutions 14/06/2018 Niranjan Babu.H.S

2 Robert Bosch Engineering and Business Solutions-EMT 100% Subsidiary of Robert Bosch Gmbh RBEI-EMT 200+ RCP, Modeling Projects, Eclipse Plugin developers Efficient, Cost effective and High Quality development centers in India Configuration, Design, Model based, M2T, M2M, Graphics, DSL s, ALM, Build use case expertise 2 RBEI/EMT

3 Agenda Motivation Why EMF in Current Challenges Overview Architecture Default Algorithms and Tooling Demo Q & A 3

4 Motivation Using ML to Catalyse Software Development Productivity,Quality and Efficiency Improvement Tooling Image Source 4

5 Why EMF in? A P P L I C A T Network Design Component Design Configuration Tooling Code Generation Software Build Report Generation I O N P R O J E C T S 5

6 Current Problems Over Engineering Human Learning Complexity Complex IDE s 6

7 Learning Data Source ECU Artifacts Connected Cars Data Source Expert Brains Service logs 7

8 What is? Framework to support plugging in Machine Learning and AI algorithms easily into model based tooling Tooling AI DPL ML 8

9 Components Learning Component AI and Application Component UI User Features User Alg User Alg User Alg User Alg Child Descriptors Algo Manager Parsing & Learning Extensions ML Libs Model Traversal Export Import Artop Adapt Item Provider Application API s Libs AI Algorithm Manager Meta Model Database Manager Cache Model Manager Model Learning MONGO / MYSQL Meta Model 9

10 Extension Types Parsing (1) Parse & Interpret the data to Model Knowledge Repository Meta Model ML (1.*) Learn from Model Knowledge Repository AI (1..*) Apply & 10 RBEI/EMT

11 Meta-Model Structure Class Data Set Data Point 11

12 Meta-Model Sample Data Set D A T A P Fuel Type diesel Wheel Base continuous from Vehicle D A T A 0 O I N T S Engine Size continuous from 61 to 326 <<Class>> S E T S Data Set 1 D A T A P O I N Fuel Type Petrol Wheel Base continuous from Engine Size continuous from 61 to 326 T S 12

13 EMF Plugged in Features Item Providers New Child Descriptors Constraint Generator Use case Assistance In-Progress Error Assistance In-Progress 13 RBEI/EMT

14 Default Algorithms Best Values Possible Values Range Prediction 14

15 Sample K-Cluster & Sample Liner Regression Algorithms 15

16 Constraint Generator Rules Validate Apps Input Deep Learning Deep Learning 16

17 Live Demo 17

18 New Child Descriptors Complex Default Proposal based on learning from usage Simplified 18 RBEI/EMT

19 Item Providers Default Proposal based on learning Complex Simplified 19 RBEI/EMT

20 Best Value Proposal Proposal based on learning Predicting Best Data 20 RBEI/EMT

21 Use case Prediction- Auto Generate Network Data Possible Decision path Auto generated Network configuration By Algorihim Console 21 RBEI/EMT

22 Constraint Derivation Validated Data is just 50% complete 22 RBEI/EMT

23 Dash Board- Best Value, Min-Max and Possible Values Possible Options Best Min Max 23 RBEI/EMT

24 Dash Board-Predicting Possible Children and weight 24 RBEI/EMT

25 Dash Board Linear and Cluster Widgets 25 RBEI/EMT

26 Q & A 26

27 Thank You 27

28 Workflow Step 1 P Define & Contribute Parsing Algorithm Step 3 M Export Algorithm Models Step 5 A Integrate To User Application Step 2 L Define & Contribute ML Algorithm Meta Model Data Points Knowledge Repository Meta Model Algorithm Data Points Step 4 I Define & Contribute AI Algorithm Step 6 M Import Algorithm Models Learning Component AI and Application Component 28 RBEI/EMT