SWI-Prolog is neither a commercial Prolog system nor a purely academic enterprise, but increasingly a community project. The core system has been shaped to its current form while being used as a tool for building research prototypes, primarily for knowledge- intensive and interactive systems. Community contributions have added several interfaces and the constraint (CLP) libraries. Commercial involvement has created the initial garbage collector, added several interfaces and two development tools: PlDoc (a literate program- ming documentation system) and PlUnit (a unit testing environment). In this article we present SWI-Prolog as an integrating tool, supporting a wide range of ideas developed in the Prolog community and acting as glue between foreign resources. This article itself is the glue between technical articles on SWI-Prolog, providing context and experience in applying them over a longer period

The Social Golfer Problem (SGP) is a sports scheduling problem that exhibits a lot of symmetry and has recently attracted significant attention. In this paper, we first revisit an existing SAT encoding for the SGP and correct some of its clauses. We then propose a change in the encoding that significantly reduces the number of variables for all instances. We achieve considerable performance improvements when solving many SGP instances with common SAT solvers using local search and complete backtracking. This makes SAT formulations a more promising approach for solving the SGP than previously.

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The Social Golfer Problem (SGP) is a combinatorial optimization problem that exhibits a lot of symmetry and has recently attracted significant attention. In this paper, we present a new greedy heuristic for the SGP, based on the intuitive concept of freedom among players. We use this heuristic in a complete backtracking search, and match the best current results of constraint solvers for several SGP instances with a much simpler method. We then use the main idea of the heuristic to construct initial configurations for a metaheuristic approach, and show that this significantly improves results obtained by local search alone. In particular, our method is the first metaheuristic technique that can solve the original problem instance optimally. We show that our approach is also highly competitive with other metaheuristic and constraint-based methods on many other benchmark instances from the literature.