a reinforcement learning based self-healing algorithm for managing context adaptation
In this paper we approach the context adaptation problem by defining a self-healing model that uses a policy-driven reinforcement learning mechanism to take run-time decisions. The self-healing property is enforced by monitoring the system's execution environment for evaluating the degree of fulfilling the context policies in the current context situation, and selecting the healing actions that may be executed. For action selection a reinforcement learning based approach is used to generate the best sequence of actions to be taken for keeping the context resources as close as possible to a policy compliant state.
reinforcement learning self-healing context adaptation