Neural approximators and team theory for dynamic routing: a receding-horizon approach

The problem of optimal dynamic routing of messages in a store-and-forward packet switching network is addressed by a receding-horizon approach. The nodes of the network must make routing decisions on the basis of local information and possibly of some data, received from other nodes and compute their routing strategies by measuring local variables and exchanging a small amount of data with other nodes. These tasks lead to regard the nodes as the cooperating decision makers of a team organization, and call for a computationally distributed algorithm. The well known impossibility of solving team optimal control problems under general conditions suggest two main approximating assumptions: 1) the team optimal control problem is stated in a receding-horizon framework; and 2) each decision maker acting at a node is assigned a given structure in which a finite number of parameters have to be determined in order to minimize the cost function. This makes it possible to approximate the original functional optimization problem by a nonlinear programming one and to compute off line the routing control strategies