CMODELS – SAT-based Disjunctive Answer Set Solver

Disjunctive logic programming under the stable model semantics [GL91] is a new methodology called answer set programming (ASP) for solving combinatorial search problems. This programming method uses answer set solvers, such as DLV [Lea05], GNT [Jea05], SMODELS [SS05], ASSAT [LZ02], CMODELS [Lie05a]. Systems DLV and GNT are more general as they work with the class of disjunctive logic programs, while other systems cover only normal programs. DLV is uniquely designed to find the answer sets for disjunctive logic programs. On the other hand, GNT first generates possible stable model candidates and then tests the candidate on the minimality using system SMODELS as an inference engine for both tasks. Systems CMODELS and ASSAT use SAT solvers as search engines. They are based on the relationship between the completion semantics [Cla78], loop formulas [LZ02] and answer set semantics for logic programs. Here we present the implementation of a SAT-based algorithm for finding answer sets for disjunctive logic programs within CMODELS. The work is based on the definition of completion for disjunctive programs [LL03] and the generalisation of loop formulas [LZ02] to the case of disjunctive programs [LL03].We propose the necessary modifications to the SAT based ASSAT algorithm [LZ02] as well as to the generate and test algorithmfrom [GLM04] in order to adapt them to the case of disjunctive programs. We implement the algorithms in CMODELS and demonstrate the experimental results

Loop Formulas for Description Logic Programs

Description Logic Programs (dl-programs) proposed by Eiter et al. constitute an elegant yet powerful formalism for the integration of answer set programming with description logics, for the Semantic Web. In this paper, we generalize the notions of completion and loop formulas of logic programs to description logic programs and show that the answer sets of a dl-program can be precisely captured by the models of its completion and loop formulas. Furthermore, we propose a new, alternative semantics for dl-programs, called the {\em canonical answer set semantics}, which is defined by the models of completion that satisfy what are called canonical loop formulas. A desirable property of canonical answer sets is that they are free of circular justifications. Some properties of canonical answer sets are also explored.Comment: 29 pages, 1 figures (in pdf), a short version appeared in ICLP'1

A Logic of Reachable Patterns in Linked Data-Structures

We define a new decidable logic for expressing and checking invariants of programs that manipulate dynamically-allocated objects via pointers and destructive pointer updates. The main feature of this logic is the ability to limit the neighborhood of a node that is reachable via a regular expression from a designated node. The logic is closed under boolean operations (entailment, negation) and has a finite model property. The key technical result is the proof of decidability. We show how to express precondition, postconditions, and loop invariants for some interesting programs. It is also possible to express properties such as disjointness of data-structures, and low-level heap mutations. Moreover, our logic can express properties of arbitrary data-structures and of an arbitrary number of pointer fields. The latter provides a way to naturally specify postconditions that relate the fields on entry to a procedure to the fields on exit. Therefore, it is possible to use the logic to automatically prove partial correctness of programs performing low-level heap mutations

Fifty years of Hoare's Logic

We present a history of Hoare's logic.Comment: 79 pages. To appear in Formal Aspects of Computin

Backdoors to Normality for Disjunctive Logic Programs

Over the last two decades, propositional satisfiability (SAT) has become one of the most successful and widely applied techniques for the solution of NP-complete problems. The aim of this paper is to investigate theoretically how Sat can be utilized for the efficient solution of problems that are harder than NP or co-NP. In particular, we consider the fundamental reasoning problems in propositional disjunctive answer set programming (ASP), Brave Reasoning and Skeptical Reasoning, which ask whether a given atom is contained in at least one or in all answer sets, respectively. Both problems are located at the second level of the Polynomial Hierarchy and thus assumed to be harder than NP or co-NP. One cannot transform these two reasoning problems into SAT in polynomial time, unless the Polynomial Hierarchy collapses. We show that certain structural aspects of disjunctive logic programs can be utilized to break through this complexity barrier, using new techniques from Parameterized Complexity. In particular, we exhibit transformations from Brave and Skeptical Reasoning to SAT that run in time O(2^k n^2) where k is a structural parameter of the instance and n the input size. In other words, the reduction is fixed-parameter tractable for parameter k. As the parameter k we take the size of a smallest backdoor with respect to the class of normal (i.e., disjunction-free) programs. Such a backdoor is a set of atoms that when deleted makes the program normal. In consequence, the combinatorial explosion, which is expected when transforming a problem from the second level of the Polynomial Hierarchy to the first level, can now be confined to the parameter k, while the running time of the reduction is polynomial in the input size n, where the order of the polynomial is independent of k.Comment: A short version will appear in the Proceedings of the Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI'13). A preliminary version of the paper was presented on the workshop Answer Set Programming and Other Computing Paradigms (ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest, Hungar

Simultaneous Replacement in Normal Programs

The simultaneous replacement transformation operation is here defined and studied w.r.t. normal programs. We give applicability conditions able to ensure the correctness of the operation w.r.t. the set of logical consequences of the completed database. We consider separately the cases in which the underlying language is infinite and finite; in this latter case we also distinguish according to the kind of domain closure axioms adopted. As corollaries we obtain results for Fitting's and Kunen's semantics. We also show how simultaneous replacement can mimic other transformation operations such as thinning, fattening and folding, thus producing applicability conditions for them too

Invariant Synthesis for Incomplete Verification Engines

We propose a framework for synthesizing inductive invariants for incomplete verification engines, which soundly reduce logical problems in undecidable theories to decidable theories. Our framework is based on the counter-example guided inductive synthesis principle (CEGIS) and allows verification engines to communicate non-provability information to guide invariant synthesis. We show precisely how the verification engine can compute such non-provability information and how to build effective learning algorithms when invariants are expressed as Boolean combinations of a fixed set of predicates. Moreover, we evaluate our framework in two verification settings, one in which verification engines need to handle quantified formulas and one in which verification engines have to reason about heap properties expressed in an expressive but undecidable separation logic. Our experiments show that our invariant synthesis framework based on non-provability information can both effectively synthesize inductive invariants and adequately strengthen contracts across a large suite of programs

Proof Relevant Corecursive Resolution

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 role 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 non-terminating type class resolution problems.Comment: 23 pages, with appendices in FLOPS 201