Abstract
The burgeoning field of Artificial Intelligence (AI) is deeply intertwined with the principles of the Theory of Computation (ToC), offering a rich landscape for algorithmic exploration and theoretical inquiry. This research article delves into the intricate relationship between algorithms and AI from a ToC perspective, aiming to uncover fundamental insights that underpin the development and understanding of intelligent systems. Commencing with a foundational overview of ToC principles, including automata theory, computability, and complexity theory, the paper elucidates how these concepts serve as the bedrock for modelling and analysing AI algorithms. Through a comprehensive survey it investigates diverse algorithmic paradigms within AI, encompassing machine learning, optimization, and decision-making. Moreover, the article examines how ToC provides a formal framework for characterizing the computational resources and constraints underlying AI algorithms, thereby shedding light on their theoretical capabilities and limitations.
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