Unification Algorithm Implementation for First-Order Logic Inference Engines

A unification algorithm is one of the most important parts of a First-Order Logic (FOL) inference engine because it allows for the discovery of substitutions that make two logical expressions identical. This process, in fact, is the very foundation for the work of successful automated reasoning and knowledge representation systems: such systems work by manipulating logical formulas in the tasks of theorem proving, query answering, and updating a knowledge base. Traditionally, unification uses heuristic and manual approaches that are very time-consuming and prone to errors. But recent advances in AI and machine learning techniques applied along with some image processing techniques open new scopes within which the process of unification may be made more efficient and accurate. This paper describes how the unification algorithm can be put into practice for inference engines built on FOL, focusing on how it facilitates the automation of logical deduction. We discuss a range of approaches from using CNN for the detection of very subtle logical relationships and differences in expressions. Put emphasis on recursive unification to cope with compound terms and nested structures. Methods proposed generally indicate rather serious efficiency improvements for scalability and reliability in automated reasoning systems. The unification procedure is more consistent and reproducible and can deal with large-scale logical problems if AI is applied.