Intelligent System in some Decision Classes’ Environment

Authors

  • Mariusz Swiecicki AGH University of Science and Technology

DOI:

https://doi.org/10.7494/csci.1999.1.1.3573

Abstract

At the end of our century, we can observe revolution in the field of information processing. We are currently witnessing an increase in the amount of data that human beings use in their daily lives, as well as an increase in the possibilities of data processing. We can easily say that the above phenomena are not only dependent on each other, but also interact with each other. In a situation of rapid growth in both the amount of information and computing power, the question may be asked how to improve our perception of reality by having such resources at our disposal. Until now, in the process of learning about the world around him, he used methods whose bases were developed at the end of the last century, thanks to which he had numerical procedures that could be used, among others, in such areas as speech recognition, image recognition to control systems in certain narrowly specialized classes of issues. Notwithstanding this, artificial intelligence research was carried out in many scientific laboratories. Thanks to the results of this work, it was possible to create various types of expert and predictive systems that were based on symbolic calculus, or used numerical techniques. Restrictions on this type of system may include: the use of established numerical procedures, small opportunities to implement the learning process, or rather self-learning, as well as the lack of a universal system architecture to solve problems in various fields. In this way we come to the issues that will be the subject of this article.

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References

Ashby W.: Wstęp do cybernetyki. Warszawa, PWN 1961

Bateson G.: Umysł iprzyroda. Warszawa, Państwowy Instytut Wydawniczy 1996

Corkhill D.D.: Hierarchicalplanning in distributed environment. Proceedings of the

Sixth intemational Joint Conference on Artificial Intelligence, Vols 1 and 2,Tokyo, 1979, Los Altos, CA: Morgan Kaufmann, 168-175.

Demazeau Y., Miiler J.: Decentralized Artificial Intelligence. In Demazeu Y. (Ed.) Decentralised Al. Proceedings of The First European Workshop on Modeling Autonomous Agents in a Multi-Agent World, Cambridge, England, 1989

Fritz W.: Intelligent Systems. New Horizons Press 1996

Goldberg D.E.: Algorytmy genetyczne i ich zastosowanie. Warszawa, WNT 1995

Grabowska A., Budohoska F., Kozielecki J.: Percepcja. Myślenie. Decyzje. Warszawa,

PWN 1995

Greniweski H.: Cybernetyka niematematyczna. Warszawa, PWN 1969

Hertz J., Krogh A., Palmer R. G.: Wstęp do teorii obliczeń neuronowych. Warszawa, WNT 1995

Huhns M.N. (Ed.): DistributedArtificial Intelligence. Morgan Kaufmann 1987

Korbicz J., Obuchowski A., Uciński D.: Sztuczne sieci neuronowe podstawy i zasto sowania. Warszawa, Akademicka Oficyna Wydawnicza PLJ 1994

Kurcz I.: Pamięć. Uczenie się. Język. Warszawa, PWN 1995

Lizotte M., Moulin M.: A Temporal Planning For Modellling Autonomous Agents. In Demazeu Y. (Ed.) Decentralized Al. Proceedings of The First European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Cambridge, England, 1989

Martial F.: Interactions Among Autonomous Planning Agents. In Demazeu Y. (Ed.) Decentralized Al. Proceedings of The First European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Cambridge, England, 1989

Maruichi T., Tokoro M.: Modeling Autonomous Agents and Their Groups. In Demazeu Y. (Ed.) Decentralized Al. Proceedings of The First European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Cambridge, England, 1989

Mazur M.: Cybernetyczna teoria układów samodzielnych. Warszawa, PWN 1966 Mazur M.: Cybernetyka i charakter. Warszawa, AULA 1996

StrelauJ.: Temperament i Inteligencja. Warszawa, Wydawnictwo Naukowe PWN 1996 Święcicki M.: Autonomiczni agenci oraz wzajemne oddziaływania pomiędzy planami agentów. Kraków, Zeszyty Naukowe AGH, Elektrotechnika 3/1996

Święcicki M.: Typy współpracy w środowisku autonomicznych agentów. Kraków, Zeszyty Naukowe AGH, Elektrotechnika 2/1997

Tadeusiewicz R.: Sieci neuronowe. Warszawa, Akademicka Oficyna Wydawnicza RM 1993

Wajs W., Święcicki M.: Neuron model ofan autonomous agent. Proceedings of the Third Conference Neural Networks and Their applications and Summer School on Neural Networks Applications to Signal Processing, KULE 97, Częstochowa, Poland Wajs W.,

Święcicki M.: Neural NetWork Model ofAutonomous Agentfor Decision Support System. International Congress on Modeiling and Simulation MODSIM 97, AUSTRALIA

Werner E.: Distributed cooperation Algorithms. In Demazeu Y. (Ed.) Decentralized AI. Proceedings of The First European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Cambridge, 1989 England

Wilensky R.: Planning and understanding. Massachusetts, Addison-Wesley, Reading,

Włodarski Z.: Psychologia uczenia się. Warszawa, PWN 1996

urada J., Barski M., Jędruch W.: Sztuczne sieci neuronowe podstawy teorii i zasto sowanie. Warszawa, PWN 1996

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Published

2020-01-02

How to Cite

Swiecicki, M. (2020). Intelligent System in some Decision Classes’ Environment. Computer Science, 1(1). https://doi.org/10.7494/csci.1999.1.1.3573

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