Logic Programming (LP) stands as a cornerstone in the realm of Artificial Intelligence (AI), offering powerful tools for knowledge representation, reasoning and problem-solving. This paper delves into the intricate landscape of LP within AI, elucidating its foundational principles, essential algorithms and diverse applications. Beginning with a primer on LP, we traverse through its historical evolution, highlighting key milestones and pivotal developments. Subsequently, we dissect fundamental LP paradigms, such as Prolog, Constraint Logic Programming (CLP) and Answer Set Programming (ASP), elucidating their unique features and operational mechanisms. The discourse then shifts towards exploring LP algorithms, including resolution, unification and constraint propagation, unraveling their roles in facilitating logical inference and deduction. Through a comprehensive survey of LP applications, ranging from expert systems and natural language processing to robotics and bioinformatics, we underscore the versatility and efficacy of LP in addressing real-world challenges. Moreover, we analyze contemporary trends and emerging research directions, charting the trajectory of LP's evolution amidst the ever-expanding landscape of AI. By shedding light on LP's theoretical underpinnings, algorithmic intricacies and practical implications, this paper aims to provide a holistic understanding of its significance in shaping the future of AI.
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