An Evolutionary Approach to Provide Flexible Decision Dialogues in Intelligent Decision Support Systems

In order to appropriately tackle the complexity of real world problems, decision makers often use special support tools. Comprising an important class of such tools, intelligent decision support systems (iDSS) are able to not only help on the decision making process, but also improve their performance through time. Very often the use of intelligent techniques in iDSS focuses only on the reasoning mechanism. However, more than in conventional systems, a flexible interface can unleash abilities not commonly afforded to the decision maker. Flexibility here is a means to facilitate the acquisition of: (i) problem information requirements and (ii) profile of computer-user interaction. This work puts it out an interaction model based on evolutionary computation that is able to provide semi-automatic parameterization of decision trees of iDSS. As a proof of concept, experiments were conducted using four benchmark databases including several distinct features and decision scenarios. Results suggest that the proposed method is indeed useful to provide good interface adaptation (i.e. flexibility). Our approach made easier the decision task as problem information requirements and interaction profile were gathered and utilized to reframe the interface.