Evolution of Artificial Intelligence in Decision Support Systems

This document is a white paper that outlines the evolution of artificial intelligence (AI) in decision support systems (DSS). It traces the development of foundational models such as Selfridge’s Pandemonium and perceptrons, which have influenced modern machine learning and deep learning systems. The paper details the divergence of AI approaches into expert-based systems and machine learning, discussing the trade-offs between accuracy, scalability, and explainability. It further examines classification-based decision systems, including decision trees and Random Forests, and highlights the role of Complex Event Processing (CEP) in enhancing real-time decision-making. The concept of Decision Intelligence Platforms (DIPs) is introduced, showcasing their ability to integrate data, analytics, and AI techniques for improved decision-making. The paper concludes with a discussion on Cogility’s Cogynt.ai platform, which exemplifies the integration of historical AI concepts in a modern DSS, emphasizing its capabilities in handling high-risk decisions through explainable and scalable solutions.