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Inteligentny system w wybranej klasie dynamicznych środowisk decyzyjnych

Święcicki M.

Computer Science

1999

Vol. 1

57-102

Jedną z tendencji w informatyce lat ostatnich jest dynamiczny rozwój zdecentralizowanych inteligentnych systemów informatycznych. Są to systemy na tyle złożone, że klasyczne metody opisu ich dynamiki stają się niewystarczające. Jednym z możliwych sposobów opisu jest zastosowanie modelu agenta, który stanowi niezależny byt, posiada swoją listę celów, umiejętność oddziaływania na swoje środowisko oraz adaptacji do zmian zachodzących w środowisku.

One of the trends in the information theory of recent years is a dynamic development of decentralized, intelligent information systems. The systems in question are so complex that the classical methods of describing their dynamics, prove to be insufficient. One of the possible ways of describing them is by applying the model of an agent which constitutes an independent entity existing in its own right. The agent has its own inventory of objectives and the ability to interact with the eivironment and adapt to the changes which take place in this environment. The paper presents the conception of autonomous agent as a cybernetic model of an autonomous system. It is based on psychological and biological phenomena which may be observed in living organisms. The description of the system distinguishes between three basic functional blocks: correlator, homeostasis and accumulator; it contains a detailed account of the functions and mutual correlations and interactions. Subsequently, the paper presents a elassification of interactions between correlator and homeostatis, as well as between homeostasis and accumulator. An analysis of the autonomous system from the point of view of the psychological and biological phenomena which take place in it, has allowed one to borrow certain educational methods from behavioural psychology (classical conditioning, instrumental conditioning) and apply them to the teaching of these type of systems. The paper presents the implementation of these type of systems in artificial neuron networks. Subsequently, the authors show how in practical terms these type of systems may be taught by means of the above-mentioned methods. Lastly, the paper presents the application of the above systems to the learning process together with the discussion of the accompanying phenomena.