Prolog comes from the era (the 70s) where computationally interesting and algorithmically complex problems were a proportionately much larger part of the comput

Prolog comes from the era (the 70s) where computationally interesting and algorithmically complex problems were a proportionately much larger part of the computing scene than they are today, what with our personal computers, data crunching, and web applications. It’s not really that those kinds of computing paradigms are bad so much as they aren’t nearly as relevant as they were. And a typical programmer, even if they do encounter a problem appropriate for that kind of approach, is much better off using the typical software they have tons of experience with, rather than attempting to learn a new paradigm and uncovering all its pitfalls in the process of learning it. Exhaustively searching an exponential space with no heuristics for reducing the search space is obviously gonna be way easier in a familiar language than in a new language. The one use case I do see for prolog and similar tools in the modern day is for Constraint Satisfaction programming. Obviously, exhaustively searching an exponential solution space won’t be better for that, but typically you can use concepts like Edge and Arc consistency (which are core to CSP solvers) to greatly reduce the actual search space. Implementing those algorithms from scratch in an imperative language is annoyingly hard, so reaching for optimized, generalized implementations can be a good choice. For something like a Sudoku solver, edge/arc consistency can greatly improve compute times. If you’re good at sudoku, you are already implementing these techniques when solving puzzles, just like good chess players implement minimax search without having to be told what minimax search is. https://www.sciencedirect.com/topics/computer-science/arc-consistency https://www.sciencedirect.com/topics/computer-science/arc-co...