Design and Evaluation of a Multiagent Autonomic Information System

The goal of an autonomic system is to self-manage itself and adjust its actions in the face of environmental changes. In this paper, we adopt a multiagent approach to developing an autonomic information system. The aim of this autonomic information system (AIS) is to provide an information system that can adjust its processing algorithms and/or information sources to provide required information at various levels of efficiency and effectiveness. Our approach to developing autonomic multiagent systems is based on the organization model for adaptive computational systems. We describe the design of one particular autonomic system, the AIS, and illustrate how this system fulfills certain desired autonomic properties. We also evaluate the performance of our autonomic system by comparing it to a non- autonomic system.

[1]  Paul M. Torrens,et al.  Geographic Automata Systems , 2005, Int. J. Geogr. Inf. Sci..

[2]  Roy Sterritt,et al.  Towards autonomic computing: effective event management , 2002, 27th Annual NASA Goddard/IEEE Software Engineering Workshop, 2002. Proceedings..

[3]  Philip R. Cohen,et al.  Towards a fault-tolerant multi-agent system architecture , 2000, AGENTS '00.

[4]  Jacques Ferber,et al.  Organization models and behavioural requirements specification for multi-agent systems , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[5]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[6]  Yixin Diao,et al.  ABLE: A toolkit for building multiagent autonomic systems , 2002, IBM Syst. J..

[7]  Nichols Hall An Organizational Model and Dynamic Goal Model for Autonomous, Adaptive Systems , 2006 .

[8]  Petr Jan Horn,et al.  Autonomic Computing: IBM's Perspective on the State of Information Technology , 2001 .

[9]  Axel van Lamsweerde,et al.  Managing Conflicts in Goal-Driven Requirements Engineering , 1998, IEEE Trans. Software Eng..

[10]  Roy Sterritt,et al.  Towards an Autonomic Computing Environment , 2003, DEXA Workshops.

[11]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[12]  Carol O'Sullivan,et al.  Geopostors: a real-time geometry / impostor crowd rendering system , 2005, I3D '05.

[13]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[14]  Scott A. DeLoach,et al.  An Investigation of Reorganization Algorithms , 2006, IC-AI.

[15]  Alan Penn,et al.  Encoding Natural Movement as an Agent-Based System: An Investigation into Human Pedestrian Behaviour in the Built Environment , 2002 .

[16]  Rajarshi Das,et al.  A multi-agent systems approach to autonomic computing , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[17]  L. Lomnitz Formal Organizations , 1982, Current Anthropology.

[18]  Scott A. DeLoach,et al.  An Organizational Model and Dynamic Goal Model for Autonomous, Adaptive Systems , 2006 .

[19]  Thomas A. Corbi,et al.  The dawning of the autonomic computing era , 2003, IBM Syst. J..