Challenges in Relational Learning for Real-Time Systems Applications

Authors:
Mark Bartlett;Iain Bate;Dimitar Kazakov
Affiliations:
Artificial Intelligence Group, Department of Computer Science, University of York, Heslington, York, UK and Real Time Systems Group, Department of Computer Science, University of York, Heslington, ...;Real Time Systems Group, Department of Computer Science, University of York, Heslington, York, UK;Artificial Intelligence Group, Department of Computer Science, University of York, Heslington, York, UK
Venue:
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Year:
2008

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Abstract

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.