Membrane Computing Based Scalable Distributed Learning and Collaborative Decision Making for Cyber Physical Systems

Machine to Machine based communication and coordination in cyber physical systems for manufacturing application is a daunting problem. Here, the difficulty is in developing a multi task learning algorithm for finding collaborative key points within high dimensional decision space. In order to overcome such a problem a gradient based method is required to enable distributed control and collaborative decision making. This paper presents membrane computing based gradient method for online multitask learning and controlling the distributed coordination to achieve stochastic planning to formulate optimize semantic network to derive a local optimal policy. Experimental verification is achieved through implementation of several concurrent functioning of mobile robotic platforms.