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2009 | Vol. 37, nr 1 | 52-56
Tytuł artykułu

Cooperating agents approach to task execution planning

Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The aim of the paper is to investigate the behaviour of the cooperating agents having to agree task execution. Design/methodology/approach: A heuristic method is proposed for scheduling tasks that should be carried out by a group of agents. An important issue is that the agents have to complete their negotiations to commit to the task and carry out it in real time. This task was solved by splitting the problem solving into two layers which make it possible to obtain the rough solutions first and further improve them next as much as possible. Findings: The first layer proposed refers to tasks assigned static priorities and its goal is to meet the real time requirements. Next, the agents can proceed to obtain the optimized solution, provided there is time to do it before the task execution has to begin. Research limitations/implications: Analysis of the negotiations procedure was done and model examples were worked out to develop a planning system based on these design requirements. Practical implications: Implementing the real time task execution planning in artificial agents brings them closer to the job shop floor real life requirements, making it possible to develop software entities mimicking reactions of humans and capable of joint task execution planned on the fly as needed. Originality/value: Analysis of the real time task planning and execution of groups of agents.
Wydawca

Rocznik
Strony
52-56
Opis fizyczny
Bibliogr. 23 poz., rys., tabl.
Twórcy
autor
  • Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland, janusz.madejski@polsl.pl
Bibliografia
  • [1] M. Wooldridge, N. R. Jennings, (Eds.), Agent theories, architectures, and languages: a survey. In: Intelligent Agents, ECAI’94 Workshop on Theories, Architectures, and Languages. Springer, Berlin,1995.
  • [2] J. Madejski, State-of-the art of Distributed Artificial Intelligence in Manufacturing Systems, Proceedings of the 5th International Conference Achievements in Mechanical and Materials Engineering AMME'96, Wisła, 1996 121-124.
  • [3] J. Madejski, Agents as the Building Blocks of the Responsibility-Based Manufacturing Systems, Proceedings of the International Scientific Conference - Challenges to Civil and Mechanical Engineering, Wrocław, 1997, 213-220.
  • [4] W. C., Benton, H. Shin, Manufacturing planning and control: The evolution of MRP and JIT integration. European Journal of Operational Research 110 (1998) 411-440.
  • [5] J. Madejski, Fuzzy logic approach to the autonomous agent task utility function evaluation, Proceedings of the 8th International Conference "Achievements in Mechanical and Materials Engineering" AMME'99, Gliwice-Rydzyna- Pawłowice-Rokosowo, 1999, 241-244.
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  • [8] M. Shimbo, T. Ishida, Controlling the learning process of real-time heuristic search. Artificial Intelligence 146/1 (2003) 1-41.
  • [9] F. Barber, V. Botti, A. Crespo, D. Gallardo, E. Onaindi, A temporal blackboard for real-time process control. Journal of Engineering Applications of Artificial Intelligence 7/3 (1994) 225-266.
  • [10] F. Baykoc, S. Erol, Simulation modelling and analysis of a JIT production system, International Journal of Production Economics 55 (1998) 203-212.
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  • [12] R. E. White, J. N. Pearson, J. R. Wilson, JIT manufacturing: A survey of implementations in small and large US manufacturers, Management Science 45/1 (1999) 1-15.
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  • [14] C. Carrascosa, M. Rebollo, J. Julian, V. Botti, Deliberative server for real-time agents, Lecture Notes in Artificial Intelligence 2691 (2003) 485-496.
  • [15] C. Carrascosa, J. Bajo, V. Julian, J. M. Corchado, V. Botti, Hybrid multi-agent architecture as a real-time problem-solving model, Expert Systems with Applications 34 (2006) 2-17.
  • [16] Y. Qian, M. Zheng, X. Li, L. Lin, Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation. Expert Systems with Applications 28 (2005) 249-257.
  • [17] S. Ossowski, Co-ordination in Artificial Agent Societies, Springer-Verlag, Berlin, Heidelber, New York, 1998.
  • [18] http://www.qnx.com/
  • [19] V. Botti, C. Carrascosa, V. Julian, J. Soler, Modelling agents in hard real-time environments. Lectures Notes in Artificial Intelligence 1647 (1999) 63-76.
  • [20] A. Garvey, V. Lesser, Design-to-time real-time scheduling. IEEE Transactions on Systems, Man and Cybernetics 23/6 (1993) 1491-1503.
  • [21] A. Garvey, V. Lesser, A Survey of research in deliberative real-time artificial intelligence, Journal of Real-Time Systems 6/2 (1994) 317-347.
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  • [23] F. Barber, V. Botti, A. Crespo, D. Gallardo, E. Onaindi, A temporal blackboard for real-time process control. Journal of Engineering Applications of Artificial Intelligence 7/3 (1994) 225-266.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS2-0021-0007
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