Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICAS.2008.35 | ICAS |
Keywords | Field | DocType |
planning process,control variable,autonomic software,abstruse problem,appropriate action,adaptive action selection,autonomic software system,reinforcement learning,action selection technique,software behavior,action selection,mathematical model,software systems,space exploration,application software,learning artificial intelligence,simulation model,software quality,software performance | Software design,Computer science,Software system,Artificial intelligence,Software metric,Action selection,Software construction,Machine learning,Goal-Driven Software Development Process,Software development,Reinforcement learning | Conference |
Citations | PageRank | References |
20 | 0.91 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehdi Amoui | 1 | 68 | 6.02 |
| Mazeiar Salehie | 2 | 831 | 34.30 |
| Siavash Mirarab | 3 | 292 | 16.84 |
| Ladan Tahvildari | 4 | 1408 | 68.51 |