Managing Uncertainty in Self-Adaptive Systems with Plan Reuse
and Stochastic Search
Cody Kinneer, Zack Coker, Jiacheng Wang,
David Garlan and Claire Le Goues.
In Proceedings of the 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 28-29 May 2018. Selected as a distinguished paper.
Online links:   Plain Text
Abstract
| Many software systems operate in environments where change
and uncertainty are the rule, rather than exceptions. Techniques
for self-adaptation allow these systems to automatically respond
to environmental changes, yet they do not handle changes to the
adaptive system itself, such as the addition or removal of adaptation
tactics. Instead, changes in a self-adaptive system often require a
human planner to redo an expensive planning process to allow
the system to continue satisfying its quality requirements under
different conditions; automated techniques typically must replan
from scratch. We propose to address this problem by reusing prior
planning knowledge to adapt in the face of unexpected situations.
We present a planner based on genetic programming that reuses
existing plans. While reuse of material in genetic algorithms has
recently applied successfully in the area of automated program
repair, we find that naïvely reusing existing plans for self-* planning
actually results in a loss of utility. Furthermore, we propose a
series of techniques to lower the costs of reuse, allowing genetic
techniques to leverage existing information to improve planning
utility when replanning for unexpected changes. |
Keywords: Planning, Self-adaptation, Stochastic Search, uncertainty.
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