RT Journal Article
JF 2008 4th International Conference on Autonomic and Autonomous Systems
YR 2008
VO 00
IS
SP 175
TI Adaptive Action Selection in Autonomic Software Using Reinforcement Learning
A1 Mehdi Amoui,
A1 Mazeiar Salehie,
A1 Siavash Mirarab,
A1 Ladan Tahvildari,
K1 Self Adaptive Software
K1 Reinforcement Learning
K1 Action Selection
AB The planning process in autonomic software aims at selecting an action from a finite set of alternatives for adaptation. This is an abstruse problem due to the fact that software behavior is usually very complex with numerous number of control variables. This research work focuses on proposing a planning process and specifically an action selection technique based on "Reinforcement Learning" (RL). We argue why, how, and when RL can be beneficial for an autonomic software system. The proposed approach is applied to a simulated model of a news web application. Evaluation results show that this approach can learn to select appropriate actions in a highly dynamic environment. Furthermore, we compare this approach with another technique from the literature, and the results suggest that it can achieve similar performance in spite of no expert involvement.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN
LA English
DO 10.1109/ICAS.2008.35
LK http://doi.ieeecomputersociety.org/10.1109/ICAS.2008.35