RecS

RecS, short for Recursive Search System, is a modular software framework intended to support recursive problem solving across domains such as artificial intelligence, optimization, and natural language processing. It provides a formal abstraction for representing problems as recursive search trees, along with facilities for state management, move generation, pruning, and evaluation.

The architecture of RecS typically includes a core engine that orchestrates the search, adapters that translate domain-specific problems into the RecS representation, a set of pluggable search strategies (for example, depth-first or best-first) and a heuristic module that assigns scores to partial solutions. A memoization layer and conflict-pruning mechanisms help mitigate redundant work in large search spaces. Communication between components follows well-defined interfaces, supporting reuse across different problem domains.

In operation, a problem is encoded as an initial state, with rules to generate successor states. The core engine repeatedly expands states, applies heuristics to prioritize promising branches, and backtracks when dead ends or constraint violations are detected. Optional parallel exploration and caching can improve performance on large problems.

RecS is used in areas including constraint satisfaction, planning and scheduling, combinatorial optimization, and parsing tasks that benefit from systematic exploration of alternatives. It is also employed in research to compare search strategies and to prototype domain-specific solvers before committing to a bespoke implementation.

Limitations include potential exponential growth of the search tree, sensitivity to heuristic quality, and overhead from abstraction layers. As with other search frameworks, successful use depends on appropriate problem formulation, effective domain adapters, and careful tuning of strategies.

See also: constraint programming, search algorithm, recursion, heuristic search.