A crisis management approach to mission survivability in computational multi-agent systems

• Autorzy: Byrski A. , Kisiel-Dorohinicki M. , Carvalho M.

Warianty tytułu

PL

Utrzymanie krytycznych zadań oparte na zarządzaniu kryzysowym w obliczeniowych systemach wieloagentowych

• Języki publikacji: EN

Abstrakty

EN

In this paper we present a biologically-inspired approach for mission survivability (considered as the capability of fulfilling a task such as computation) that allows the system to be aware of the possible threats or crises that may arise. This approach uses the notion of resources used by living organisms to control their populations. We present the concept of energetic selection in agent-based evolutionary systems as well as the means to manipulate the configuration of the computation according to the crises or user's specific demands.

PL

W artykule prezentujemy biologicznie inspirowany mechanizm wspomagający utrzymanie krytycznych zadań (tzw. mission survivability) który umożliwia wykrywanie oraz przeciwdziałanie wybranym zagrożeniom. Przedstawione podejście wzorowane jest na wykorzystywaniu przez żywe organizmy zasobów do kontroli populacji. Prezentujemy koncepcje selekcji energetycznej mającej zastosowanie w ewolucyjnych systemach wieloagentowych (EMAS) oraz sposoby konfiguracji obliczenia w celu przeciwdziałania sytuacjom kryzysowym, według preferencji użytkownika.

Słowa kluczowe

EN

agent systems; crisis management; soft computing

PL

systemy agentowe; zarządzanie kryzysowe; soft computing

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2010
• Tom: Vol. 11
• Strony: 99--113
• Opis fizyczny: Bibliogr. 24 poz., rys., wykr.

Twórcy

autor

Byrski A.

• Department of Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland

autor

Kisiel-Dorohinicki M.

• Department of Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland

autor

Carvalho M.

• Institute for Human & Machine Cognition, 40 South Alcaniz Street Pensacola, FL 32502, USA

Bibliografia

• [1] Byrski A., Kisiel-Dorohinicki M.: Immunological selection mechanism in agent-based evolutionary computation. Proc. of Information Processing and Web Mining IIPWM'05, Gdansk, Poland, 2005.
• [2] Byrski A., Kisiel-Dorohinicki M.: Agent-based evolutionary and immunological optimization. Proc. of International Conference on Computational Science ICCS 2007, Beijing, China, 2007.
• [3] Byrski A., Kisiel-Dorohinicki M.: User-assisted management of agent-based evolutionary computation. Proc. of International Conference on Computational Science ICCS 2008, Krakow, Poland, 2008.
• [4] Byrski A., Schaefer R.: Immunological mechanism for asynchronous evolutionary computation boosting. Proc. of European Workshop on Intelligent Computational Methods and Applied Mathematics ICMAM 2008, Krakow, Poland, 2008.
• [5] Byrski A., Schaefer R.: Formal model for agent-based asynchronous evolutionary computation. Proc. of IEEE World Congress on Computational Intelligence, Trondheim, Norway, 2009.
• [6] Byrski A., Schaefer R.: Stochastic model of evolutionary and immunological multi-agent systems: Mutually exclusive actions. Fundamenta Informaticae, vol. 94, 2009.
• [7] Cantii-Paz E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois, 1995.
• [8] Cetnarowicz K., Kisiel-Dorohinicki M., Nawarecki E.: The application of evolution process in multi-agent world (MAW) to the prediction system. Proc. of the 2nd International Conference on Multi-Agent Systems ICMAS'96, Kyoto, Japan, 1996.
• [9] Dobrowolski G., Kisiel-Dorohinicki M.: Management of evolutionary MAS for multiobjective optimization. Proc. of Evolutionary Methods in Mechanics, 2004.
• [10] Dobrowolski G., Kisiel-Dorohinicki M., Nawarecki E.: Monitoring as a means for discovery of crises in MAS. Proc. of 12th IFAC symposium on INformation COntrol problems in Manufacturing INCOM, 2006.
• [11] Dreżewski R.: A co-evolutionary multi-agent system for multi-modal function optimization. Proc. of International Conference on Computational Science, Kraków, Poland, 2004.
• [12] Ellison R. J., Fischer D. A. et al.: Survivable network systems: An emerging discipline. Technical Report, Carnegie Mellon University, 1999.
• [13] Ellison R. J., Goodenough J., et al.: Survivability assurance for system of systems. Technical report, Carnegie Mellon University, 2008.
• [14] GoldbergD.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, 1989.
• [15] Jennings N.R., Sycara. K., Wooldrdige M.J.: A roadmap of agent research and development. Journal of Autonomous Agents and Multi-Agent Systems, vol. 1, 1998, pp. 7-38.
• [16] Jennings N.R., Wooldridge M. J.: Software agents. IEE Review, 1996, pp. 17-20.
• [17] Kisiel-Dorohinicki M.: Agent-oriented model of simulated evolution. Proc. of Theory and Practice of Informatics SofSem 2002, Milovy, Czech Republic, 2002.
• [18] Kisiel-Dorohinicki M.: Monitoring in multi-agent systems: two perspectives. Monitoring, security and rescue techniques in multiagent systems. Springer Verlag, Advances in Soft Computing, 2005.
• [19] Lovelock J.E.: Gaia as seen through the atmosphere. Atmospheric Environment,vol. 6, 1972.
• [20] Marieb E. N., Hoehn K.: Human Anatomy & Physiology. Pearson Benjamin Cummings, 2007.
• [21] Nawarecki E., Kisiel-Dorohinicki M., Dobrowolski G.: Architecture for discovery of crises in MAS. Fundamenta Informaticae, vol. 71, 2006.
• [22] Putman R., Wratten S.D.: Principles of Ecology. University of California Press, 1992.
• [23] Schaefer R., Byrski A., Smołka M.: Stochastic model of evolutionary and immunological multi-agent systems: Parallel execution of local actions. Fundamenta Informaticae, vol. 94, 2009.
• [24] Siwik L., Dreżewski R.: Agent-based multi-objective evolutionary algorithms with cultural and immunological mechanisms. Evolutionary computation, In-Teh, 2009.