Case-based Reasoning in Agent-based Decision Case-based Reasoning in Agent-based Decision

The paper describes decision support system for modeling, control, andsimulation of continuous, as well as discrete event systems. Models, control methods, andtools are in database specified by their attributes. Each attribute’s weight is initiallyestimated according to importance and classification power of a given feature. Automaticlearning of attributes weights uses the answers of the users after simulation provided by thesystem to increase the quality of case-based reasoning. The proposed learning algorithmguarantees convergence of the attributes weights to relatively steady values yielding toCase-based reasoning of best quality. If simulation of a new case was successful from thepoint of view of the user, the new case is added to case base.