Induction of search control rules for hard real-time task scheduling

The determination of heuristics control rules to direct search algorithms is usually an ad hoc domain dependent process, where complexity increases with the number of factors to take into account. In this paper, we presented a method for learning control rules using tree induction algorithms. The approach was applied to construct a scheduler for periodic tasks with timing and precedence constraints. An experimental evaluation shows that the scheduler is able to learn effective search control rules from previous experiences, improving its performance.