Abstract

The study of anti-addiction modeling through reinforcement and decision support (RDS) systems is a developing area that leverages technology to address addiction-related behaviors by fostering healthier decision-making patterns. This annotated bibliography presents a summary of current literature on RDS anti-addiction modeling, covering various methodologies, findings, and implications for clinical and technological advancements. The selected works explore a range of topics, including the efficacy of RDS frameworks in addiction treatment, the role of artificial intelligence (AI) in enhancing therapeutic interventions, and ethical concerns surrounding automated decisionmaking in mental health. By synthesizing these works, this paper identifies critical trends and gaps in the literature, highlighting future directions for research and practice in the field of addiction treatment using RDS technology