Applying process control and machine learning to develop effective dynamic decision strategies Conference Paper uri icon


  • Dynamic decision making (DDM) requires an agent to make a series of pathdependent, time-critical decisions in environments that change as a result of the agent’s actions as well as autonomously. We model DDM as a process control problem using a general-purpose “measure-intervene-iterate” framework and propose a machine learning approach to improving decision strategies. We present results from applying the technique in a simulation of an important healthcare DDM problem, namely diabetes care.

publication date

  • 2011