17,052 research outputs found
Linear-Logic Based Analysis of Constraint Handling Rules with Disjunction
Constraint Handling Rules (CHR) is a declarative committed-choice programming
language with a strong relationship to linear logic. Its generalization CHR
with Disjunction (CHRv) is a multi-paradigm declarative programming language
that allows the embedding of horn programs. We analyse the assets and the
limitations of the classical declarative semantics of CHR before we motivate
and develop a linear-logic declarative semantics for CHR and CHRv. We show how
to apply the linear-logic semantics to decide program properties and to prove
operational equivalence of CHRv programs across the boundaries of language
paradigms
Logic Programming for Describing and Solving Planning Problems
A logic programming paradigm which expresses solutions to problems as stable
models has recently been promoted as a declarative approach to solving various
combinatorial and search problems, including planning problems. In this
paradigm, all program rules are considered as constraints and solutions are
stable models of the rule set. This is a rather radical departure from the
standard paradigm of logic programming. In this paper we revisit abductive
logic programming and argue that it allows a programming style which is as
declarative as programming based on stable models. However, within abductive
logic programming, one has two kinds of rules. On the one hand predicate
definitions (which may depend on the abducibles) which are nothing else than
standard logic programs (with their non-monotonic semantics when containing
with negation); on the other hand rules which constrain the models for the
abducibles. In this sense abductive logic programming is a smooth extension of
the standard paradigm of logic programming, not a radical departure.Comment: 8 pages, no figures, Eighth International Workshop on Nonmonotonic
Reasoning, special track on Representing Actions and Plannin
Core TuLiP
We propose CoreTuLiP - the core of a trust management language based on Logic Programming. CoreTuLiP is based on a subset of moded logic programming, but enjoys the features of TM languages such as RT; in particular clauses are issued by different authorities and stored in a distributed manner. We present a lookup and inference algorithm which we prove to be correct and complete w.r.t. the declarative semantics. CoreTuLiP enjoys uniform syntax and the well-established semantics and is expressive enough to model scenarios which are hard to deal with in RT
Hybrid Rules with Well-Founded Semantics
A general framework is proposed for integration of rules and external first
order theories. It is based on the well-founded semantics of normal logic
programs and inspired by ideas of Constraint Logic Programming (CLP) and
constructive negation for logic programs. Hybrid rules are normal clauses
extended with constraints in the bodies; constraints are certain formulae in
the language of the external theory. A hybrid program is a pair of a set of
hybrid rules and an external theory. Instances of the framework are obtained by
specifying the class of external theories, and the class of constraints. An
example instance is integration of (non-disjunctive) Datalog with ontologies
formalized as description logics.
The paper defines a declarative semantics of hybrid programs and a
goal-driven formal operational semantics. The latter can be seen as a
generalization of SLS-resolution. It provides a basis for hybrid
implementations combining Prolog with constraint solvers. Soundness of the
operational semantics is proven. Sufficient conditions for decidability of the
declarative semantics, and for completeness of the operational semantics are
given
Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging
Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving
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