Applications of Mathematical Techniques to Artificial Intelligence

Abstract

Artificial intelligence (AI) is a computer program, system, or technology developed based on mathematical models, algorithms, and computer programs that can think, perform tasks, make decisions, or act like a human being. Problems in AI fall into different broad categories: Planning Problems, Search Problems, Representation Problems, etc. Rules, frames, logics, and nets are interconnected models and tools that follow after them. They are all highly mathematical subjects. The basic goal of AI is to construct a model that humans can comprehend. These models can be constructed using concepts and techniques from many fields of mathematics/statistics. With the aid of mathematical principles, thinking machines and the ability to imitate human behavior are conceptualized. For example, a self-driving car or robot recognizes people and objects in visuals. AI utilizes mathematics (Operations research) in the form of minimization algorithms and back-propagation for these automobiles. Utilizing centuries-old methodologies and techniques, mathematics enables AI scientists to address difficult, deep, abstract issues using traditional methods and procedures. Mathematics and AI are two branches of the same tree, and mathematics is the foundation of AI advancement. In this paper, we describe the numerous fields of mathematics/statistics employed in AI to gain a coherent understanding of mathematics/statistics and AI. The present work contributes to the literature by giving a new, up-to-date, thorough, and innovative literature evaluation to integrate, synthesize, and provide future research recommendations.