Abstract:
Developing autonomous multi agent systems are to be considered an advancement of multi agent systems can be
applied in both physical and logical world. Constructions of multi hazard risk assessment using spatial data for
disaster management have a problem of effective communication because of implicit knowledge. Risk
management is the identification, assessment, and prioritization of risks followed by coordinated and
economical application of resources to minimize, monitor, and control the probability and/or impact of
unfortunate events. Constructions of risk assessment using spatial data for disaster management have a
problem of effective communication because of implicit knowledge Risk assessment is the determination of
quantitative or qualitative value of risk related to a concrete situation and a recognized hazard. Quantitative risk
assessment requires commonsense knowledge related with the hazard. This complicates the effective
communication of data to the user in real-time machine processing in support of disaster management. In this
paper we present an approach to modeling commonsense knowledge in qualitative risk assessment using
Autonomous multi agent system. This gives three-phase knowledge modeling approach for modeling
commonsense knowledge in, which enables holistic approach for disaster management. At the initial stage
commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are
modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic
module, which is constructed on the basis of principal components. Further explanations for classified
knowledge are derived by expert system technology. We have implemented the system using FLEX expert
system shell, SPSS, XML and VB. This paper describes one such approach using classification of human
constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy.