An ontological framework for knowledge modeling and decision support in cyber-physical systems

被引:48
作者
Petnga, Leonard [1 ]
Austin, Mark [1 ,2 ]
机构
[1] Univ Maryland, Dept Civil & Environm Engn, College Pk, MD 20742 USA
[2] Univ Maryland, Syst Res Inst, College Pk, MD 20742 USA
关键词
Ontologies; Cyber-physical systems; Reasoning; Decision making; Artificial intelligence; Semantic Web; OWL;
D O I
10.1016/j.aei.2015.12.003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Our work is concerned with the development of knowledge structures to support correct-by-design cyber-physical systems (CPS). This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance and safety, and a reliance on automation for the management of system functionality and decision making. To assure correctness of functionality with respect to requirements, there is a strong need for system models to account for semantics of the domains involved. This paper introduces a new ontological-based knowledge and reasoning framework for decision support for CPS. It enables the development of determinate, provable and executable CPS models supported by sound semantics strengthening the model-driven approach to CPS design. An investigation into the structure of basic description logics (DL) has identified the needed semantic extensions to enable the web ontology language (OWL) as the ontological language for our framework. The SROIQ DL has been found to be the most appropriate logic-based knowledge formalism as it maps to OWL 2 and ensures its decidability. Thus, correct, stable, complete and terminating reasoning algorithms are guaranteed with this SROIQ-backed language. The framework takes advantage of the commonality of data and information processing in the different domains involved to overcome the barrier of heterogeneity of domains and physics in CPS. Rules-based reasoning processes are employed. The framework provides interfaces for semantic extensions and computational support, including the ability to handle quantities for which dimensions and units are semantic parameters in the physical world. Together, these capabilities enable the conversion of data to knowledge and their effective use for efficient decision making and the study of system-level properties, especially safety. We exercise these concepts in a traffic light time-based reasoning system. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:77 / 94
页数:18
相关论文
共 67 条
[1]  
A. A. C. U, 2010, ETH REAS VAL RUBR
[2]  
Allen J. F., 1982, P 20 ANN M ASS COMP, P19, DOI [10.3115/981251.981256, DOI 10.3115/981251.981256]
[3]   MAINTAINING KNOWLEDGE ABOUT TEMPORAL INTERVALS [J].
ALLEN, JF .
COMMUNICATIONS OF THE ACM, 1983, 26 (11) :832-843
[4]  
[Anonymous], STRAT VIS BUS DRIV 2
[5]  
Baader F, 2003, DESCRIPTION LOGIC HANDBOOK: THEORY, IMPLEMENTATION AND APPLICATIONS, P43
[6]  
Baader F, 2008, FOUND ARTIF INTELL, P135, DOI 10.1016/S1574-6526(07)03003-9
[7]  
Baader Franz., 1991, Proceedings of the 12th International Joint Conference on Artificial Intelligence. Sydney, Australia, August 24-30, P452
[8]  
Baader R, 2005, LECT NOTES ARTIF INT, V2605, P228
[9]  
Bhave A., 2010, Multi-domain Modeling of Cyber-Physical Systems Using Architectural Views
[10]  
Bock C. E., 2010, ADV ENG INFORM