A General Implementation Framework for Tabled CLP

Guzmán, Pablo Chico de; Carro, Manuel; Hermenegildo, Manuel V.; Stuckey, Peter J.

doi:10.1007/978-3-642-29822-6_11

Cited by 7 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the integration of tabling and CLP makes it possible to execute the dist/3, right, using constraint entailment [4] to suspend calls which are more particular than previous calls, and to keep only the most general answers. The query ?-D #< K, dist(a,Y,D) terminates under TCLP because by placing the constraint D #< K before dist(a,Y,D), the search is pruned when the values in D are larger than or equal to K.…”

Section: Background: Tabling and Constraintsmentioning

confidence: 99%

Incremental Evaluation of Lattice-Based Aggregates in Logic Programming Using Modular TCLP

Arias

Carro

2018

Practical Aspects of Declarative Languages

Self Cite

View full text Add to dashboard Cite

Aggregates are used to compute single pieces of information from separate data items, such as records in a database or answers to a query to a logic program. The maximum and minimum are well-known examples of aggregates. The computation of aggregates in Prolog or variant-based tabling can loop even if the aggregate at hand can be finitely determined. When answer subsumption or mode-directed tabling is used, termination improves, but the behavior observed in existing proposals is not consistent. We present a framework to incrementally compute aggregates for elements in a lattice. We use the entailment and join relations of the lattice to define (and compute) aggregates and decide whether some atom is compatible with (entails) the aggregate. The semantics of the aggregates defined in this way is consistent with the LFP semantics of tabling with constraints. Our implementation is based on the TCLP framework available in Ciao Prolog, and improves its termination properties w.r.t. similar approaches. Defining aggregates that do not fit into the lattice structure is possible, but some properties guaranteed by the lattice may not hold. However, the flexibility provided by this possibility justifies its inclusion. We validate our design with several examples and we evaluate their performance.

show abstract

Section: Background: Tabling and Constraintsmentioning

confidence: 99%

Incremental Evaluation of Lattice-Based Aggregates in Logic Programming Using Modular TCLP

Arias

Carro

2018

Practical Aspects of Declarative Languages

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, we write the CLP clauses defining the predicate unsafe that holds if and only if there exists an execution of the program P that leads from an initial configuration to an error configuration. 10. unsafe :-initConf(X), reach(X).…”

Section: Encoding Safety Problems Of Imperative Programs Using Clpmentioning

confidence: 99%

“…Instead, consider an IHC solver following the approach of Failure Tabled CLP (FTCLP) [23]. In a nutshell, FTCLP augments tabled CLP [10,12] by computing interpolants whenever a failed derivation CLP constraints for Figure 1 is encountered during the top-down evaluation of the CLP program. If a call of predicate p has been fully executed and has not produced any solution then its interpolants denote the conditions under which the execution of p will always lead to failure.…”

Section: An Introductory Examplementioning

confidence: 99%

Verification of Programs by Combining Iterated Specialization with Interpolation

Angelis¹,

Fioravanti²,

Navas³

et al. 2014

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

We present a verification technique for program safety that combines Iterated Specialization and Interpolating Horn Clause Solving. Our new method composes together these two techniques in a modular way by exploiting the common Horn Clause representation of the verification problem. The Iterated Specialization verifier transforms an initial set of verification conditions by using unfold/fold equivalence preserving transformation rules. During transformation, program invariants are discovered by applying widening operators. Then the output set of specialized verification conditions is analyzed by an Interpolating Horn Clause solver, hence adding the effect of interpolation to the effect of widening. The specialization and interpolation phases can be iterated, and also combined with other transformations that change the direction of propagation of the constraints (forward from the program preconditions or backward from the error conditions). We have implemented our verification technique by integrating the VeriMAP verifier with the FTCLP Horn Clause solver, based on Iterated Specialization and Interpolation, respectively. Our experimental results show that the integrated verifier improves the precision of each of the individual components run separately.

show abstract

“…Although we currently support only variant tabling under swapping evaluation, Ciao can combine tabled LP and CLP [4], and work to combine them with once/1 under swapping evaluation is underway. The resulting system can be applied, for example, to a declarative and efficient formulation of optimization.…”

Section: Application To Minimization Problemsmentioning

confidence: 99%

“…Also, variant tabling is assumed, i.e., a call is considered a consumer iff it is identical, modulo variable renaming, to a previous generator. Adapting the proposed solutions to work under subsumptive tabling, which considers a goal A to be a consumer of a goal B if B θ ≡ A for some substitution θ (or, in general, for tabling under constraints where consumers are defined under the notion of entailment [4]) is left for future work. We assume some familiarity with the WAM [5] and proper tabling implementations.…”

Section: Introductionmentioning

confidence: 99%

Supporting Pruning in Tabled LP

Guzmán¹,

Carro

Hermenegildo

2013

Practical Aspects of Declarative Languages

Self Cite

View full text Add to dashboard Cite

Abstract. This paper analyzes issues which appear when supporting pruning operations in tabled LP. A version of the once/1 control predicate tailored for tabled predicates is presented, and an implementation is discussed and evaluated. The use of once/1 with answer-on-demand strategies makes it possible to avoid computing unneeded solutions for problems which can benefit from tabled LP but in which only a single solution is needed, such as model checking and planning. The proposed version of once/1 is also directly applicable to the efficient implementation of other optimizations, such as early completion, cut-fail loops (to, e.g., perform pruning at the toplevel), if-then-else, and constraint-based branch-and-bound optimization. Although once/1 still presents open issues such as dependencies of tabled solutions on program history, our experimental evaluation confirms that it provides an arbitrarily large efficiency improvement in several application areas.

show abstract

A General Implementation Framework for Tabled CLP

Cited by 7 publications

References 24 publications

Incremental Evaluation of Lattice-Based Aggregates in Logic Programming Using Modular TCLP

Incremental Evaluation of Lattice-Based Aggregates in Logic Programming Using Modular TCLP

Verification of Programs by Combining Iterated Specialization with Interpolation

Supporting Pruning in Tabled LP

Contact Info

Product

Resources

About