Challenges in Relational Learning for Real-Time Systems Applications

TY - GEN

T1 - Challenges in Relational Learning for Real-Time Systems Applications

AU - Bartlett, Mark

AU - Bate, Iain

AU - Kazakov, Dimitar

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Worst Case Execution Time (WCET)

KW - Inductive Logic Programming (ILP)

KW - Lazy Learning

KW - Symmetry

KW - Efficiency

KW - SEARCH

UR - http://www.scopus.com/inward/record.url?scp=52149122530&partnerID=8YFLogxK

M3 - Conference contribution

SN - 978-3-540-85927-7

VL - 5194 LNAI

SP - 42

EP - 58

BT - INDUCTIVE LOGIC PROGRAMMING, ILP 2008

A2 - Zelezny, F

A2 - Lavrac, N

PB - Springer

CY - BERLIN

T2 - 18th International Conference on Inductive Logic Programming

Y2 - 10 September 2008 through 12 September 2008

ER -