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

Advanced type system features, such as GADTs, type classes, and type families have proven to be invaluable language extensions for ensuring data invariants and program correctness among others. Unfortunately, they pose a tough problem for type inference, because they introduce local type assumptions.In this article we present a novel constraint-based type inference approach for local type assumptions. Our system, called OutsideIn(X), is parameterised over the particular underlying constraint domain X, in the same way as HM(X). This stratification allows us to use a common metatheory and inference algorithm.Going beyond the general framework, we also give a particular constraint solver for X = type classes + GADTs + type families, a non-trivial challenge in its own right.

This is a summary of the Ph.D. thesis of Tom Schrijvers [4].Constraint Handling Rules (CHR) [3] is a rule-based language commonly embedded in a host language. It combines elements of Constraint Logic Programming and term rewriting. Several implementations of CHR exist: in Prolog, Haskell, Java and HAL. Typical applications of CHR are in the area of constraint solving, but currently CHR is also used in a wider range of applications, such as type checking, natural language processing and multi-agent systems.In this work we contribute program analyses, program optimizations and extensions of the CHR language.For the optimized compilation of CHR we present several new optimizations: code specialization for ground constraints, anti-monotonic delay avoidance, hashtable constraint stores and a new late storage optimization. These and other optimizations have been implemented in a new state-of-the-art CHR system: the K.U.Leuven CHR system [5] which is currently available in SWI-Prolog [10], XSB [9] and hProlog [2].Abstract interpretation is a general technique for program analysis [1]. We propose a framework of abstract interpretation for the CHR language [7], in particular for the formulation of analyses that drive program optimization. This frameworks allows for the uniform formulation of program analyses as well as easier improvements and combinations of existing analyses. We also evaluate analyses for theoretical properties, confluence and time complexity, on a practical case study to investigate their relevance.We contribute two extensions to the expressivity of CHR. The first extension comprises the integration of CHR with tabled execution [8]. Tabled execution avoids many forms of non-termination and is useful for automatic program optimization through the dynamic reuse of previous computations. The second extension automatically provides implication checking functionality to constraint solvers written in CHR [6]. Implication checking is an essential building block for formulating complex constraints in terms of basic constraints and for composing constraint solvers.

We report on an extension of Haskell with open type-level functions and equality constraints that unifies earlier work on GADTs, functional dependencies, and associated types. The contribution of the paper is that we identify and characterise the key technical challenge of entailment checking; and we give a novel, decidable, sound, and complete algorithm to solve it, together with some practically-important variants. Our system is implemented in GHC, and is already in active use.

Resolution lies at the foundation of both logic programming and type class context reduction in functional languages. Terminating derivations by resolution have well-defined inductive meaning, whereas some non-terminating derivations can be understood coinductively. Cycle detection is a popular method to capture a small subset of such derivations. We show that in fact cycle detection is a restricted form of coinductive proof, in which the atomic formula forming the cycle plays the rôle of coinductive hypothesis. This paper introduces a heuristic method for obtaining richer coinductive hypotheses in the form of Horn formulas. Our approach subsumes cycle detection and gives coinductive meaning to a larger class of derivations. For this purpose we extend resolution with Horn formula resolvents and corecursive evidence generation. We illustrate our method on nonterminating type class resolution problems.