FuzzyBandit An Autonomous Personalized Model Based on Contextual Multi Arm Bandits Using Explainable AI

Abstract

In the era of artificial cognizance, context-aware decision-making problems have attracted significant attention. Contextual bandit addresses these problems by solving the exploration versus exploitation dilemma faced to provide customized solutions as per the user’s liking. However, a high level of accountability is required, and there is a need to understand the underlying mechanism of the black box nature of the contextual bandit algorithms proposed in the literature. To overcome these shortcomings, an explainable AI (XAI) based FuzzyBandit model is proposed, which maximizes the cumulative reward by optimizing the decision at each trial based on the rewards received in previous observations and, at the same time, generates explanations for the decision made. The proposed model uses an adaptive neuro-fuzzy inference system (ANFIS) to address the vague nature of arm selection in contextual bandits and uses a feedback mechanism to adjust its parameters based on the relevance and diversity of the features to maximize reward generation. The FuzzyBandit model has also been empirically compared with the existing seven most popular art of literature models on four benchmark datasets over nine criteria, namely recall, specificity, precision, prevalence, F1 score, Matthews Correlation Coefficient (MCC), Fowlkes–Mallows index (FM), Critical Success Index (CSI) and accuracy.