Goal-directed execution of answer set programs

Marple, Kyle; Bansal, Ajay; Min, Richard; Gupta, Gopal

doi:10.1145/2370776.2370782

Cited by 19 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Handling global constraints is closely related to dynamic consistency checking (DCC) in goaldirected answer set programs [11,10]. DCC ensures only the necessary constraints, that are relevant to a rule and the possible bindings variables could take, are checked along with the rule-body.…”

Section: Open Challenges and Expected Achievementsmentioning

confidence: 99%

Imperative Program Synthesis from Answer Set Programs

Varanasi¹

2019

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

Our research concerns generating imperative programs from Answer Set Programming Specifications. ASP is highly declarative and is ideal for writing specifications. Further with negation-asfailure it is easy to succinctly represent combinatorial search problems. We are currently working on synthesizing imperative programs from ASP programs by turning the negation into useful computations. This opens up a novel way to synthesize programs from executable specifications.

show abstract

Section: Open Challenges and Expected Achievementsmentioning

confidence: 99%

Imperative Program Synthesis from Answer Set Programs

Varanasi¹

2019

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

show abstract

“…Within ASP, work has been done on goal-directed reasoning. Both Bonatti, Pontelli, and Son (2008) and Marple, Bansal, Min, and Gupta (2012) demonstrate approaches, in the style of SLD resolution, that apply top-down instantiation to answer queries over infinite domains. Saptawijaya and Pereira (2013) extend an abduction framework to lazily generate part of the relevant sentences.…”

Section: Related Workmentioning

confidence: 99%

Lazy Model Expansion: Interleaving Grounding with Search

Cat¹,

Denecker²,

Stuckey³

et al. 2015

jair

View full text Add to dashboard Cite

Finding satisfying assignments for the variables involved in a set of constraints can be cast as a (bounded) model generation problem: search for (bounded) models of a theory in some logic. The state-of-the-art approach for bounded model generation for rich knowledge representation languages like Answer Set Programming (ASP) and FO(·) and a CSP modeling language such as Zinc, is ground-and-solve: reduce the theory to a ground or propositional one and apply a search algorithm to the resulting theory.An important bottleneck is the blow-up of the size of the theory caused by the grounding phase. Lazily grounding the theory during search is a way to overcome this bottleneck.We present a theoretical framework and an implementation in the context of the FO(·) knowledge representation language. Instead of grounding all parts of a theory, justications are derived for some parts of it. Given a partial assignment for the grounded part of the theory and valid justications for the formulas of the non-grounded part, the justications provide a recipe to construct a complete assignment that satises the non-grounded part. When a justication for a particular formula becomes invalid during search, a new one is derived; if that fails, the formula is split in a part to be grounded and a part that can be justied. Experimental results illustrate the power and generality of this approach. IntroductionThe world is lled with combinatorial problems. These include important combinatorial optimization tasks such as planning, scheduling and rostering, combinatorics problems such as extremal graph theory, and countless puzzles and games. Solving combinatorial problems is hard, and all the methods we know to tackle them involve some kind of search.Various declarative paradigms have been developed to solve such problems. In such approaches, objects and attributes that are searched for are represented by symbols, and constraints to be satised by those objects are represented as expressions over these symbols c 2015 AI Access Foundation. All rights reserved.De Cat, Denecker, Stuckey & Bruynooghe in a declarative language. Solvers then search for values for these symbols that satisfy the constraints. This idea is found in the elds of Constraint Programming (CP) (Apt, 2003), ASP (Marek & Truszczy«ski, 1999), SAT, Mixed Integer Programming (MIP), etc. In the terminology of logic, the declarative method amounts to expressing the desired properties of a problem class by sentences in a logical theory. The data of a particular problem instance corresponds naturally to a partial interpretation (or structure). The solving process is to apply model generation, or more specically model expansion (Mitchell & Ternovska, 2005), the task of nding a structure that expands the input partial structure and satises the theory. The resulting structure is a solution to the problem. Model generation/expansion, studied for example in the eld of Knowledge Representation (KR) (Baral, 2003), is thus analogous to the task of solving constraint satisfaction problems...

show abstract

“…Under our basic goal-directed method, a partial answer set is constructed by adding both positive and negative literals as they succeed during execution. When a query succeeds and all consistency checks have been satisfied, the set of positive literals in the partial answer set is guaranteed to be a subset of some consistent answer set of the program (Marple et al 2012). This method can be applied to arbitrary ASP programs, including those with rules that contain classical negation and disjunction.…”

Section: Goal-directed Answer Set Programmingmentioning

confidence: 99%

“…This property ensures that an answer set of the subprogram, if one exists, will be a subset of some consistent answer set of P (Marple et al 2012).…”

Section: Consistency Checkingmentioning

confidence: 99%

“…Answer Set Programming (ASP) (Gelfond and Lifschitz 1988) has gained popularity as a way to develop non-monotonic reasoning applications.Three problems which prevent ASP from being adopted on a larger scale are (i) the need to compute a complete answer set regardless of the query, (ii) the ability of a minor inconsistency to render an entire knowledgebase useless, and (iii) the need to ground programs prior to execution. Our previous work with goal-directed ASP addresses the first (Marple et al 2012), and we leave the third for future work. In this paper, we address the second problem in context of goal-directed execution of answer set programs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Consistency Checking in Goal-Directed Answer Set Programming

Marple¹,

Gupta²

2014

Theory and Practice of Logic Programming

Self Cite

View full text Add to dashboard Cite

In answer set programming, inconsistencies arise when the constraints placed on a program become unsatisfiable. In this paper, we introduce a technique for dynamic consistency checking for our goal-directed method for computing answer sets, under which only those constraints deemed relevant to the partial answer set are tested, allowing inconsistent knowledgebases to be successfully queried. However, the algorithm guarantees that, if a program has at least one consistent answer set, any partial answer set returned will be a subset of some consistent answer set. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 12 pages. Accepted to ICLP 2014. To appear in Theory and Practice of Logic Programming (TPLP

show abstract

Goal-directed execution of answer set programs

Cited by 19 publications

References 16 publications

Imperative Program Synthesis from Answer Set Programs

Imperative Program Synthesis from Answer Set Programs

Lazy Model Expansion: Interleaving Grounding with Search

Dynamic Consistency Checking in Goal-Directed Answer Set Programming

Contact Info

Product

Resources

About