On the behaviour of learning algorithms in a changing environment with application to data network routing problem. (English) Zbl 0706.68018

Summary: In data networks the message has peak and slack periods and the topology may change. A learning automaton is situated at each node of the network where a routing decision must be made and directs traffic entering the node onto one of the outgoing links. Using network feedback, an automaton modifies its routing strategy to improve its link selections. This approach has the advantage over existing routing schemes of offering a simple and extremely practical feedback and updating policy. A new model of a nonstationary automaton environment is proposed and the limiting behaviour of this model is analysed. Simulation studies of automata operating in simple networks verify the analytical results.


68M10 Network design and communication in computer systems
68T05 Learning and adaptive systems in artificial intelligence
68W10 Parallel algorithms in computer science
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