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**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.

### MSC:

68M10 | Network design and communication in computer systems |

68T05 | Learning and adaptive systems in artificial intelligence |

68W10 | Parallel algorithms in computer science |

### Keywords:

packet-switched network; network routing; virtual and datagram network; circuit-switched network; learning
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\textit{A. V. Vasilakos}, Kybernetika 26, No. 4, 296--307 (1990; Zbl 0706.68018)

### References:

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This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.