A Swift library for logic programming that solves problems by describing constraints rather than writing solutions.
Logician is a Swift library that implements logic programming capabilities, allowing developers to solve constraint-based problems declaratively. Instead of writing procedural solution algorithms, you describe the characteristics of valid solutions using variables and goals, and the library's solver finds matching answers. It's particularly useful for problems like puzzle solving, graph coloring, and dependency resolution.
Swift developers working on constraint satisfaction problems, puzzle games, scheduling applications, or anyone interested in declarative programming paradigms within the Swift ecosystem.
Logician provides a pure Swift implementation of logic programming concepts, enabling declarative problem solving with type safety and integration with Swift's toolchain. Unlike procedural approaches, it allows developers to express problems naturally as constraints rather than algorithms.
Logic programming in Swift
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Allows describing solution constraints instead of procedural algorithms, as demonstrated in Sudoku and N-Queens playgrounds, making complex problems more intuitive to model.
Leverages Swift's type system for variables and goals, ensuring compile-time checks and integration with existing Swift codebases through SPM compatibility.
Provides operators like ==, !=, and distinct for expressing constraints, reducing boilerplate and accelerating development for common logic programming tasks.
Includes playgrounds and references to resources like miniKanren papers, making it a practical tool for learning logic programming concepts in Swift.
The README explicitly states it's in early stages and the implementation is likely to change, leading to potential breaking updates and unreliable for long-term projects.
Only showcases basic puzzles like Sudoku and N-Queens, lacking demonstrations for complex, production-grade constraint problems such as scheduling or dependency resolution.
Based on a functional miniKanren approach with generators, which may introduce inefficiencies compared to optimized procedural solvers for large-scale or time-sensitive constraints.
Has minimal community support, documentation, and third-party extensions compared to established logic programming languages, increasing the learning curve and maintenance effort.