An Autonomic Context Management Model Based on Machine Learning

In this paper we approach the context management problem by defining a self-healing algorithm that uses a policy-driven reinforcement learning mechanism to take run-time decisions. The situation calculus and information system theories are used to define and formalize self-healing concepts such as context situation entropy and equivalent context situations. The self-healing property is enforced by monitoring the system's execution environment to evaluate the degree of fulfilling the context policies for a context situation, and to determine the actions to be executed in order to keep the system in a consistent healthy state.