Inductive logic program synthesis with dialogs

DIALOGS (Dialogue-based Inductive and Abductive LOGic program Synthesizer) is a schema-guided synthesizer of recursive logic programs; it takes the initiative and queries a (possibly computationally naive) specifier for evidence in her/his conceptual language. The specifier must know the answers to such simple queries, because otherwise s/he wouldn't even feel the need for the synthesized program. DIALOGS call be used by any learner (including itself) that detects, or merely conjectures, the necessity of invention of a new predicate. Due to its foundation on a powerful codification of a “recursion-theory” (by means of the template and constraints of a divide-and-conquer schema), DIALOGS needs very little evidence and is very fast. © Springer-Vertag Berlin Heidelberg 1997

A Method for Compiling and Executing Expressive Assertions

Programming with assertions constitutes an effective tool to detect and correct programming errors. The ability of executing formal specifications is essential in order to test automatically a program with respect to its assertions. However, formal specifications may describe recursive models which are difficult to identify so current assertion checkers limit, in a considerable way, the expressivity of the assertion language. In this paper, we are interested in showing how transformational synthesis can help to execute “expressive” assertions of the form ∀x(r(x) ⇔ QyR(x, y)) where x is a set of variables to be instantiated at execution time, Q is an existential or universal quantifier and R a quantifier free formula in the language of a particular first-order theory A we call assertion context. The class of assertion contexts is interesting because it presents a balance between expressiveness for writing assertions and existence of effective methods for executing them by means of synthesized (definite) logic programs

On Matrices, Automata, and Double Counting

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables M, with the same constraint defined by a finite-state automaton A on each row of M and a global cardinality constraint gcc on each column of M. We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the gcc constraints from the automaton A. The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We evaluate the impact of our methods on a large set of nurse rostering problem instances

On correct program schemas

We present our work on the representation and correctness of program schemas, in the context of logic program synthesis. Whereas most researchers represent schemas purely syntactically as higher-order expressions, we shall express a schema as an open rst-order theory that axiomatises a problem domain, called a specication framework, containing an open program that represents the template of the schema. We will show that using our approach we can dene a meaningful notion of correctness for schemas, viz. that correct program schemas can be expressed as parametric specication frameworks containing templates that are steadfast, i.e. programs that are always correct provided their open relations are computed correctly. © Springer-Verlag Berlin Heidelberg 1998

On The Complexity and Completeness of Static Constraints for Breaking Row and Column Symmetry

We consider a common type of symmetry where we have a matrix of decision variables with interchangeable rows and columns. A simple and efficient method to deal with such row and column symmetry is to post symmetry breaking constraints like DOUBLELEX and SNAKELEX. We provide a number of positive and negative results on posting such symmetry breaking constraints. On the positive side, we prove that we can compute in polynomial time a unique representative of an equivalence class in a matrix model with row and column symmetry if the number of rows (or of columns) is bounded and in a number of other special cases. On the negative side, we show that whilst DOUBLELEX and SNAKELEX are often effective in practice, they can leave a large number of symmetric solutions in the worst case. In addition, we prove that propagating DOUBLELEX completely is NP-hard. Finally we consider how to break row, column and value symmetry, correcting a result in the literature about the safeness of combining different symmetry breaking constraints. We end with the first experimental study on how much symmetry is left by DOUBLELEX and SNAKELEX on some benchmark problems.Comment: To appear in the Proceedings of the 16th International Conference on Principles and Practice of Constraint Programming (CP 2010

Validity-Guided Synthesis of Reactive Systems from Assume-Guarantee Contracts

Automated synthesis of reactive systems from specifications has been a topic of research for decades. Recently, a variety of approaches have been proposed to extend synthesis of reactive systems from proposi- tional specifications towards specifications over rich theories. We propose a novel, completely automated approach to program synthesis which reduces the problem to deciding the validity of a set of forall-exists formulas. In spirit of IC3 / PDR, our problem space is recursively refined by blocking out regions of unsafe states, aiming to discover a fixpoint that describes safe reactions. If such a fixpoint is found, we construct a witness that is directly translated into an implementation. We implemented the algorithm on top of the JKind model checker, and exercised it against contracts written using the Lustre specification language. Experimental results show how the new algorithm outperforms JKinds already existing synthesis procedure based on k-induction and addresses soundness issues in the k-inductive approach with respect to unrealizable results.Comment: 18 pages, 5 figures, 2 table

Cyclic abduction of inductively defined safety and termination preconditions

We introduce cyclic abduction: a new method for automatically inferring safety and termination preconditions of heap manipulating while programs, expressed as inductive definitions in separation logic. Cyclic abduction essentially works by searching for a cyclic proof of the desired property, abducing definitional clauses of the precondition as necessary in order to advance the proof search process. We provide an implementation, Caber, of our cyclic abduction method, based on a suite of heuristically guided tactics. It is often able to automatically infer preconditions describing lists, trees, cyclic and composite structures which, in other tools, previously had to be supplied by hand

Symmetry breaking in numeric constraint problems

Symmetry-breaking constraints in the form of inequalities between variables have been proposed for a few kind of solution symmetries in numeric CSPs. We show that, for the variable symmetries among those, the proposed inequalities are but a specific case of a relaxation of the well-known LEX constraints extensively used for discrete CSPs. We discuss the merits of this relaxation and present experimental evidences of its practical interest.Postprint (author’s final draft

Combining Symmetry Breaking and Global Constraints

Abstract. We propose a new family of constraints which combine together lexicographical ordering constraints for symmetry breaking with other common global constraints. We give a general purpose propagator for this family of constraints, and show how to improve its complexity by exploiting properties of the included global constraints.

Discriminating instance generation for automated constraint model selection

One approach to automated constraint modelling is to generate, and then select from, a set of candidate models. This method is used by the automated modelling system Conjure. To select a preferred model or set of models for a problem class from the candidates Conjure produces, we use a set of training instances drawn from the target class. It is important that the training instances are discriminating. If all models solve a given instance in a trivial amount of time, or if no models solve it in the time available, then the instance is not useful for model selection. This paper addresses the task of generating small sets of discriminating training instances automatically. The instance space is determined by the parameters of the associated problem class. We develop a number of methods of finding parameter configurations that give discriminating training instances, some of them leveraging existing parameter-tuning techniques. Our experimental results confirm the success of our approach in reducing a large set of input models to a small set that we can expect to perform well for the given problem class