• DocumentCode
    2118085
  • Title

    Predictive modeling and control of DMAS

  • Author

    Brinn, Marshall ; Berliner, Jeff ; Helsinger, Aaron ; Wright, Todd

  • Author_Institution
    BBN Technol., Cambridge, MA, USA
  • fYear
    2004
  • fDate
    30-31 Aug. 2004
  • Firstpage
    83
  • Lastpage
    89
  • Abstract
    Predicting the behavior of distributed multi-agent systems (DMAS) is a blown, extremely challenging problem. In general, we are not able to make reliable quantitative predictions of the behavior that a given DMAS exhibits, even in a blown environment, due to the complex emergent effects in those systems, which often reflect chaotic interactions. Such predictability is nonetheless crucial for reliable, controlled development and deployment of such systems. We need to be able to control the behaviors of such systems, and want to optimize configurations to achieve acceptable and reliable returns of quality-of-service for an investment of resources. We describe here an approach to developing reliable predictive models for a particular class of DMAS. We have succeeded in developing such models for this class of applications and in achieving controlled behaviors and optimized configurations based on these predictive models. We discuss our approach, and results and plans for applying this approach to broader classes of applications.
  • Keywords
    distributed programming; multi-agent systems; quality of service; DMAS predictive control; DMAS predictive modeling; Internet; algorithm analysis; algorithm design; controlled behaviors; distributed multiagent systems; distributed techniques; optimized configurations; quality-of-service; software engineering techniques; software engineering tools; Algorithm design and analysis; Application software; Chaos; Control systems; Hardware; Investments; Multiagent systems; Predictive models; Quality of service; Software algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multi-Agent Security and Survivability, 2004 IEEE First Symposium on
  • Print_ISBN
    0-7803-8799-6
  • Type

    conf

  • DOI
    10.1109/MASSUR.2004.1368421
  • Filename
    1368421