Knowledge Acquisition for Importing Existing Traces to a Trace Base Management System

2018 ◽  
Vol 17 (04) ◽  
pp. 1850041
Author(s):  
Mohamed Besnaci ◽  
Tahar Bensebaa ◽  
Nathalie Guin ◽  
Pierre-Antoine Champin

Trace Base Management System (TBMS) offers processing and querying functionalities for traces that may be of interest to users of tracked systems. Our goal is to ensure the importing of various external traces into kernel for Trace-Based System (kTBS), which is a TBMS developed in the LIRIS laboratory. To overcome the problem of traces heterogeneity, we propose to define a generic collector. To this end, a user with enough knowledge of the tracked system is prompted to define its kTBS trace model and correspondences between the elements of this model and the elements of the trace to import. The system generalises the mappings previously elicited by the user through interaction to create mapping rules. After this phase, the collector will generate modelled traces from the existing ones and the already defined mapping rules.

Author(s):  
Zhaoxia Zhang ◽  
Qing Jiang ◽  
Rujing Wang ◽  
Liangtu Song ◽  
Zhengyong Zhang ◽  
...  

The acquisition, presentation and management of autonomous driving decision-making knowledge of unmanned vehicles are the key and difficult issues in the autonomous driving decision-making system of unmanned vehicles. This paper presents a knowledge model, which includes problem description layer and problem-solving knowledge layer. The automatic driving decision knowledge base of unmanned vehicle is composed of a set of knowledge models. Knowledge model supports knowledge representation and reasoning. Based on the WEB visualization knowledge modeling tool and visualization knowledge service tool, we construct the decision-making knowledge base management system for autonomous driving of unmanned vehicles and then construct the autonomous driving decision-making system of unmanned vehicles. The reasoning example shows that the knowledge base management system can effectively improve the knowledge acquisition, representation and maintenance efficiency of autonomous driving decision-making system, which is of great significance in enhancing the intelligence level of autonomous driving decision-making system.


Sign in / Sign up

Export Citation Format

Share Document