Constraint Logic Programming (CLP) is a logic programming formalism used to solve problems requiring the consideration of constraints, like resource allocation and automated planning and scheduling. It has previously been extended in various directions, for example to support fuzzy constraint satisfaction, uncertainty, or negation, with different notions of semiring being used as a unifying abstraction for these generalizations. None of these extensions have studied clauses with negation allowed in the body. We investigate an extension of CLP which unifies many of these extensions and allows negation in the body. We provide semantics for such programs, using the framework of approximation fixpoint theory, and give a detailed overview of the impacts of properties of the semirings on the resulting semantics. As such, we provide a unifying framework that captures existing approaches and allows extending them with a more expressive language.

Explaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of abstraction and interactivity with the user. We propose REASONX, an explanation method based on Constraint Logic Programming (CLP). REASONX can provide declarative, interactive explanations for decision trees, which can be the ML models under analysis or global/local surrogate models of any black-box model. Users can express background or common sense knowledge using linear constraints and MILP optimization over features of factual and contrastive instances, and interact with the answer constraints at different levels of abstraction through constraint projection. We present here the architecture of REASONX, which consists of a Python layer, closer to the user, and a CLP layer. REASONX's core execution engine is a Prolog meta-program with declarative semantics in terms of logic theories.

This volume contains the Regular Papers, Technical Communicationsand the Doctoral Consortium papers of the 35th International Conference on Log ic Programming (ICLP 2019), held in Las Cruces, New Mexico, USA, from September 20-25, 2019. Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Cont ributions are sought in all areas of logic programming, including but not restricted to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge repre sen-tation.

This paper proposes the use of Constraint Logic Programming (CLP) to model SQL queries in a data-independent abstract layer by focusing on some semantic properties for signalling possible errors in such queries. First, we define a translation from SQL to Datalog, and from Datalog to CLP, so that solving this CLP program will give information about inconsistency, tautology, and possible simplifications. We use different constraint domains which are mapped to SQL types, and propose them to cooperate for improving accuracy. Our approach leverages a deductive system that includes SQL and Datalog, and we present an implementation in this system which is currently being tested in classroom, showing its advantages and differences with respect to other approaches, as well as some performance data. This paper is under consideration for acceptance in TPLP .

Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive constraints imposed by the provided examples. We explore Recurrent Neural Network and Graph Neural Network models. We contribute a modified miniKanren, drivable by an external agent, available at https://github.com/xuexue/neuralkanren. We show that our neural-guided approach using constraints can synthesize programs faster in many cases, and importantly, can generalize to larger problems.

You wait forever for one to come along, and then two come along at once. In this case, there has been a large gap in the market for a theoretical introduction to constraint programming ever since Edward Tsang's Foundations of Constraint Satisfaction (1993) went out of print. Therefore, we are very pleased to see two books written by two of the leading researchers in this field come along to fill the gap. Constraint programming is a very active research area within AI. It is a highly successful technology for solving a wide range of combinatorial problems, including scheduling, rostering, assignment, routing, and design. A number of companies, like ILOG, Dash Optimization, and Parc Technologies, market model building and constraint programming toolkits, which are used by companies as diverse as Amazon.com, Constraint programming is a declarative style of modeling combinatorial problems in which the user identifies the decision variables, their possible domain of values, and specifies constraints over the allowed values (for example, no two of these variables can take the same value). Sophisticated but general purpose AI search techniques like constraint propagation (to prune irrelevant parts of the search tree) and dependency directed backtracking can then be used to find solutions. Given the many advances made in constraint programming over the last decade, a new text would have been needed even if Edward Tsang's book had remained in print. These two new texts are written by two of the leading researchers in this field. Principles of Constraint Programming by Krzysztof Apt contains chapters that cover topics like local consistency, constraint propagation, linear equations, interval reasoning, and search. Constraint Processing by Rina Dechter covers similar ground but also has chapters that cover topics like local search, tree decomposition methods, optimization, and probabilistic networks more extensively. Dechter's book also contains a chapter by David Cohen and Peter Jeavons on tractability and one by Francesca Rossi on constraint logic programming. There is much in common between the two books. This is perhaps not so surprising since Krzysztof Apt thanks Rina Dechter for much useful discussion that helped him enter the field and start doing research in the area.

Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a conservative extension of first-order reasoning, so that, for example, there is a one-one mapping between logical and random variables. We show how, in this framework, Inductive Logic Programming (ILP) can be used to induce the features of a loglinear model from data. We also compare the presented framework with other approaches to first-order probabilistic reasoning.

We are proud to introduce this special issue of the Journal of Theory and Practice of Logic Programming (TPLP), dedicated to the full papers accepted for the 28th International Conference on Logic Programming (ICLP). The ICLP meetings started in Marseille in 1982 and since then constitute the main venue for presenting and discussing work in the area of logic programming. We contributed to ICLP for the first time in 1991. The first guest-editor had a paper on logic programming with sets, and the second had two papers on the parallel implementation of the Andorra model. Since then, we continued pursuing research in this exciting area and ICLP has always been the major venue for our work.

We address the problem of designing programming systems to reason with and about constraints. Taking a logic programming approach, we define a class of programming languages, the CLP languages, all of which share the same essential semantic properties. From a conceptual point of view, CLP programs are highly declarative and are soundly based within a unified framework of formal semantics. This framework not only subsumes that of logic programming, but satisfies the core properties of logic programs more naturally. From a user's point of view, CLP programs have great expressive power due to the constraints which they naturally manipulate.