Answer Set Programming in Linguistics

Schüller, Peter

doi:10.1007/s13218-018-0542-z

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…Answer set programming (ASP) has its roots in the fields of logic programming and non-monotonic reasoning (Lifschitz, 2019;Gelfond and Kahl, 2014) and has been applied to a wide range of areas in artificial intelligence, among them also to natural language processing tasks (Mitra et al, 2019;Sharma, 2019;Schüller, 2018;Dzifcak et al, 2009). ASP is supported by powerful reasoning tools and offers a rich representation language that allows for recursive definitions, (strong and weak) negation, (strong and weak) constraints, aggregates, optimisation statements, and external functions (Gebser et al, 2019).…”

Section: Answer Set Programmingmentioning

confidence: 99%

Specifying and Verbalising Answer Set Programs in Controlled Natural Language

Schwitter¹

2018

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We show how a bi-directional grammar can be used to specify and verbalise answer set programs in controlled natural language. We start from a program specification in controlled natural language and translate this specification automatically into an executable answer set program. The resulting answer set program can be modified following certain naming conventions and the revised version of the program can then be verbalised in the same subset of natural language that was used as specification language. The bi-directional grammar is parametrised for processing and generation, deals with referring expressions, and exploits symmetries in the data structure of the grammar rules whenever these grammar rules need to be duplicated. We demonstrate that verbalisation requires sentence planning in order to aggregate similar structures with the aim to improve the readability of the generated specification. Without modifications, the generated specification is always semantically equivalent to the original one; our bi-directional grammar is the first one that allows for semantic round-tripping in the context of controlled natural language processing.

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Section: Answer Set Programmingmentioning

confidence: 99%

Specifying and Verbalising Answer Set Programs in Controlled Natural Language

Schwitter¹

2018

EasyChair Preprints

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“…(2016), Falkner et al . (2018), Schüller (2018), Alviano et al . (2020) for detailed descriptions of ASP applications).…”

Section: Introductionmentioning

confidence: 98%

An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Dodaro¹,

Galatà²,

Grioni³

et al. 2021

Theory and Practice of Logic Programming

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The problem of scheduling chemotherapy treatments in oncology clinics is a complex problem, given that the solution has to satisfy (as much as possible) several requirements such as the cyclic nature of chemotherapy treatment plans, maintaining a constant number of patients, and the availability of resources, for example, treatment time, nurses, and drugs. At the same time, realizing a satisfying schedule is of upmost importance for obtaining the best health outcomes. In this paper we first consider a specific instance of the problem which is employed in the San Martino Hospital in Genova, Italy, and present a solution to the problem based on Answer Set Programming (ASP). Then, we enrich the problem and the related ASP encoding considering further features often employed in other hospitals, desirable also in S. Martino, and/or considered in related papers. Results of an experimental analysis, conducted on the real data provided by the San Martino Hospital, show that ASP is an effective solving methodology also for this important scheduling problem.

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“…Most ASP solvers divide the solution process into two steps: a grounding phase, in which a ground program having the same stable models of the original program is generated, followed by a solving phase in which the answer sets are computed. The number of applications is impressive, deserving five surveys in a few years Erdem et al (2016);Dal Palù et al (2018); Falkner et al (2018); Kifer and Liu (2018); Schüller (2018).…”

Section: Introductionmentioning

confidence: 99%

Logic-Based Benders Decomposition in Answer Set Programming for Chronic Outpatients Scheduling

Cappanera¹,

Gavanelli²,

Nonato³

et al. 2023

Theory and Practice of Logic Programming

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In answer set programming (ASP), the user can define declaratively a problem and solve it with efficient solvers; practical applications of ASP are countless and several constraint problems have been successfully solved with ASP. On the other hand, solution time usually grows in a superlinear way (often, exponential) with respect to the size of the instance, which is impractical for large instances. A widely used approach is to split the optimization problem into subproblems (SPs) that are solved in sequence, some committing to the values assigned by others, and reconstructing a valid assignment for the whole problem by juxtaposing the solutions of the single SPs. On the one hand, this approach is much faster due to the superlinear behavior; on the other hand, it does not provide any guarantee of optimality: committing to the assignment of one SP can rule out the optimal solution from the search space. In other research areas, logic-Based Benders decomposition (LBBD) proved effective; in LBBD, the problem is decomposed into a master problem (MP) and one or several SPs. The solution of the MP is passed to the SPs that can possibly fail. In case of failure, a no-good is returned to the MP that is solved again with the addition of the new constraint. The solution process is iterated until a valid solution is obtained for all the SPs or the MP is proven infeasible. The obtained solution is provably optimal under very mild conditions. In this paper, we apply for the first time LBBD to ASP, exploiting an application in health care as case study. Experimental results show the effectiveness of the approach. We believe that the availability of LBBD can further increase the practical applicability of ASP technologies.

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Answer Set Programming in Linguistics

Cited by 8 publications

References 34 publications

Specifying and Verbalising Answer Set Programs in Controlled Natural Language

Specifying and Verbalising Answer Set Programs in Controlled Natural Language

An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

Logic-Based Benders Decomposition in Answer Set Programming for Chronic Outpatients Scheduling

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