Challenges in Relational Learning for Real-Time Systems Applications

被引：0

作者：

Bartlett, Mark ^{[1
,2
]}

Bate, Iain ^{[2
]}

Kazakov, Dimitar ^{[1
]}

机构：

[1] Univ York, Dept Comp Sci, Artificial Intelligence Grp, York YO10 5DD, N Yorkshire, England

[2] Univ York, Dept Comp Sci, Real Time Syst Grp, York, N Yorkshire, England

来源：

INDUCTIVE LOGIC PROGRAMMING, ILP 2008 | 2008年 / 5194卷

基金：

英国工程与自然科学研究理事会;

关键词：

Worst Case Execution Time (WCET); Inductive Logic Programming (ILP); Lazy Learning; Symmetry; Efficiency;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The problem of determining the Worse Case Execution Time (WCET) of a piece of code is a fundamental one in the Real Time Systems community. Existing methods either try to gain this information by analysis of the program code or by running extensive timing analyses. This paper presents a new approach to the problem based on using Machine Learning in the form of ILP to infer program properties based on sample executions of the code. Additionally, significant improvements in the range of functions learnable and the time taken for learning can be made by the application of more advanced ILP techniques.

引用

页码：42 / +

页数：3

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