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On probabilistic automata with structural restrictions
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We propose packet switching networks in which intelligent capabilities for routing and flow control are concentrated in the packets, rather than in the nodes and protocols. Networks which contain Cognitive Packets (CP) will be called Cognitive Packet Networks. Cognitive packets route themselves. They are assigned goals before entering the network, and pursue these goals adaptively. They learn to avoid congestion and to avoid getting lost or being destroyed. Cognitive packets learn from their own observations about the network and from the experience of other packets with whom they exchange information via mailboxes. Cognitive packets rely minimally on routers, so that network nodes only serve as buffers, mailboxes and processors. Each cognitive packet starts with a given representation of the network from which it then progressively constructs its own Cognitive Map of network state and uses it to make routing decisions. Cognitive packets can belong to classes, so that a class of packets share the same goals, use similar sets of rules, and make use of each other's experience. This paper describes CPN and shows how learning techniques can support the intelligent behavior of Cognitive Packets. Simulation results illustrate the effectiveness of these ideas.