Supervised Classification Model for Neural Correspondence to Environmental Uncertainty in Multiple Cue Judgment System with Decision Support

Human agents play an essential role in the multi-agent system (MAS) for appropriate action with information from autonomous robot agents with cutting-edge artificial intelligence (AI) technologies. Human agents can make decisions for proper action by evaluating system performance states, such as valid or invalid system performance caused by system errors under environmental uncertainties. Thus, the decision support system (DSS) equipped with certain levels of AI can be useful in MAS through data analysis on the information from agents to assist human agents. However, humans can make inappropriate decisions because of an unreliable decision-making environment under environmental uncertainty. This study aims to capture human neurological responses and then classify the cognitive states interacting with unreliable system environments, using concepts of expected and unexpected uncertainties in the multiple cue judgment system with decision support. We captured the brain’s response through EEG and then the cognitive state classification model was developed with supervised machine learning algorithms based on data captured. This study shows that K-Nearest Neighbors (KNN) shows 70 % classification performance above as the best and generally acceptable performance, and we could find that baseline classes, to remove temporal drift in EEG data preprocessing, hindered classification performances.