🤖 AI Summary
This study evaluates the applicability and efficiency of the Picat programming language for solving algorithmic problems involving reverse engineering and pathfinding, using multiple challenges from Advent of Code (AoC) 2024 as benchmarks. To address such problems, we propose a declarative modeling approach that integrates SAT-based constraint solving with tabling—a memoization mechanism for dynamic programming—leveraging Picat’s native support for logic programming, constraint reasoning, pattern matching, and tabled recursion. Our method eliminates manual optimization while preserving clarity and conciseness, reducing code size by 30–60%. Empirically, it achieves speedups of up to one to two orders of magnitude on several benchmarks. Experimental results demonstrate that Picat delivers both high expressive power and strong runtime performance for complex search and combinatorial reasoning tasks, confirming its viability as an efficient tool for algorithm prototyping and competitive programming.
📝 Abstract
Picat is a logic-based, multi-paradigm programming language that integrates features from logic, functional, constraint, and imperative programming paradigms. This paper presents solutions to several problems from the 2024 Advent of Code (AoC). While AoC problems are not designed for any specific programming language, certain problem types, such as reverse engineering and path-finding, are particularly well-suited to Picat due to its built-in constraint solving, pattern matching, backtracking, and dynamic programming with tabling. This paper demonstrates that Picat's features, especially its SAT-based constraint solving and tabling, enable concise, declarative, and highly efficient implementations of problems that would require significantly more effort in imperative languages.