Cognitive Neurosymbolic Artificial Intelligence for Complex Decision-Making: Integrating Foundation Models, Cognitive Architectures, and Knowledge

Yuxin Zi, Kaushik Roy, Amit P. Sheth

Abstract: Advances in deep learning and knowledge representation have driven the adoption of intelligent decision support systems in high-stakes domains such as health-care applications. However, traditional statistical approaches often yield opaque, correlation-driven predictions, making it difficult for experts to interpret results and take meaningful actions. To address these limitations, we propose a cognitive neurosymbolic artificial intelligence (AI) framework by combining multimodal perception, neurosymbolic reasoning (with symbolic controls), and knowledge, this paradigm enhances accuracy and robustness in complex decision-making environments. This integrated framework ensures that every recommendation follows human-like decision-making, while being grounded in verifiable, expert knowledge, a crucial attribute for critical application scenarios such as health care. We demonstrate this approach through a mental health support system, showcasing how cognitive neurosymbolic AI can improve decision-making outcomes, personalized interventions, longitudinal tracking, and actionable insights for critical applications.