Carmine Dodaro

WASP: A Native ASP Solver Based on Constraint Learning

This paper introduces WASP, an ASP solver handling disjunctive logic programs under the stable model semantics. WASP implements techniques originally introduced for SAT solving that have been extended to cope with ASP programs. Among them are restarts, conflict-driven constraint learning and backjumping. Moreover, WASP combines these SAT-based techniques with optimization methods that have been specifically designed for ASP computation, such as source pointers enhancing unfounded-sets computation, forward and backward inference operators based on atom support, and techniques for stable model checking. Concerning the branching heuristics, WASP adopts the BerkMin criterion hybridized with look-ahead techniques. The paper also reports on the results of experiments, in which WASP has been run on the system track of the third ASP Competition.

The Fourth Answer Set Programming Competition: Preliminary Report

Answer Set Programming is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, PDDL and many others. Since its first informal editions, ASP systems are compared in the nowadays customary ASP Competition. The fourth ASP Competition, held in 2012/2013, is the sequel to previous editions and it was jointly organized by University of Calabria Italy and the Vienna University of Technology Austria. Participants competed on a selected collection of benchmark problems, taken from a variety of research areas and real world applications. The Competition featured two tracks: the Model& Solve Track, held on an open problem encoding, on an open language basis, and open to any kind of system based on a declarative specification paradigm; and the System Track, held on the basis of fixed, public problem encodings, written in a standard ASP language.

Advances in WASP

ASP solvers address several reasoning tasks that go beyond the mere computation of answer sets. Among them are cautious reasoning, for modeling query entailment, and optimum answer set computation, for supporting numerical optimization. This paper reports on the recent improvements of the solver wasp, and details the algorithms and the design choices for addressing several reasoning tasks in ASP. An experimental analysis on publicly available benchmarks shows that the new version of wasp outperforms the previous one. Comparing with the state-of-the-art solver clasp, the performance of wasp is competitive in the overall for number of solved instances and average execution time.

Anytime computation of cautious consequences in answer set programming

Query answering in Answer Set Programming (ASP) is usually solved by computing (a subset of) the cautious consequences of a logic program. This task is computationally very hard, and there are programs for which computing cautious consequences is not viable in reasonable time. However, current ASP solvers produce the (whole) set of cautious consequences only at the end of their computation. This paper reports on strategies for computing cautious consequences, also introducing anytime algorithms able to produce sound answers during the computation.

Anytime answer set optimization via unsatisfiable core shrinking

Unsatisfiable core analysis can boost the computation of optimum stable models for logic programs with weak constraints. However, current solvers employing unsatisfiable core analysis either run to completion, or provide no suboptimal stable models but the one resulting from the preliminary disjoint cores analysis. This drawback is circumvented here by introducing a progression based shrinking of the analyzed unsatisfiable cores. In fact, suboptimal stable models are possibly found while shrinking unsatisfiable cores, hence resulting into an anytime algorithm. Moreover, as confirmed empirically, unsatisfiable core analysis also benefits from the shrinking process in terms of solved instances. This paper is under consideration for acceptance in TPLP.

Combining Answer Set Programming and domain heuristics for solving hard industrial problems (Application Paper)

Answer Set Programming (ASP) is a popular logic programming paradigm that has been applied for solving a variety of complex problems. Among the most challenging real-world applications of ASP are two industrial problems defined by Siemens: the Partner Units Problem (PUP) and the Combined Configuration Problem (CCP). The hardest instances of PUP and CCP are out of reach for state-of-the-art ASP solvers. Experiments show that the performance of ASP solvers could be significantly improved by embedding domain-specific heuristics, but a proper effective integration of such criteria in off-the-shelf ASP implementations is not obvious. In this paper the combination of ASP and domain-specific heuristics is studied with the goal of effectively solving real-world problem instances of PUP and CCP. As a byproduct of this activity, the ASP solver WASP was extended with an interface that eases embedding new external heuristics in the solver. The evaluation shows that our domain-heuristic-driven ASP solver finds solutions for all the real-world instances of PUP and CCP ever provided by Siemens. This paper is under consideration for acceptance in TPLP.

Allotment Problem in Travel Industry: A Solution Based on ASP

In the travel industry it is common for tour operators to pre-book from service suppliers blocks of package tours, which are called allotments in jargon. The selection of package tours is done according to several preference criteria aimed at maximizing the expected earnings given a budget. In this paper we formalize an allotment problem that abstracts the requirements of a real travel agent, and we solve it using Answer Set Programming. The obtained specification is executable, and it implements an advanced feature of the iTravel+ system.

The ASP System DLV2

We introduce Open image in new window , a new Answer Set Programming (ASP) system. Open image in new window combines Open image in new window , a fully-compliant ASP-Core-2 grounder, with the well-assessed solver Open image in new window . Input programs may be enriched by annotations and directives that customize heuristics of the system and extend its solving capabilities. An empirical analysis conducted on benchmarks from past ASP competitions shows that Open image in new window outperforms the old Open image in new window system and is close to the state-of-the-art ASP system \(\textsc {clingo} \).

Constraints, lazy constraints, or propagators in ASP solving: An empirical analysis

Answer Set Programming (ASP) is a well-established declarative paradigm. One of the successes of ASP is the availability of efficient systems. State-of-the-art systems are based on the ground+solve approach. In some applications this approach is infeasible because the grounding of one or few constraints is expensive. In this paper, we systematically compare alternative strategies to avoid the instantiation of problematic constraints, that are based on custom extensions of the solver. Results on real and synthetic benchmarks highlight some strengths and weaknesses of the different strategies. (Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)

Nurse Scheduling via Answer Set Programming

The Nurse Scheduling problem (NSP) is a combinatorial problem that consists of assigning nurses to shifts according to given practical constraints. In previous years, several approaches have been proposed to solve different variants of the NSP. In this paper, an ASP encoding for one of these variants is presented, whose requirements have been provided by an Italian hospital. We also design a second encoding for the computation of “optimal” schedules. Finally, an experimental analysis has been conducted on real data provided by the Italian hospital using both encodings. Results are very positive: the state-of-the-art ASP system clingo is able to compute one year schedules in few minutes, and it scales well even when more than one hundred nurses are considered.