49,930 research outputs found
Probabilistic inductive constraint logic
AbstractProbabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we consider the models produced by the inductive constraint logic system, represented by sets of integrity constraints, and we propose a probabilistic version of them. Each integrity constraint is annotated with a probability, and the resulting probabilistic logical constraint model assigns a probability of being positive to interpretations. To learn both the structure and the parameters of such probabilistic models we propose the system PASCAL for "probabilistic inductive constraint logic". Parameter learning can be performed using gradient descent or L-BFGS. PASCAL has been tested on 11 datasets and compared with a few statistical relational systems and a system that builds relational decision trees (TILDE): we demonstrate that this system achieves better or comparable results in terms of area under the precision–recall and receiver operating characteristic curves, in a comparable execution time
Layered Fixed Point Logic
We present a logic for the specification of static analysis problems that
goes beyond the logics traditionally used. Its most prominent feature is the
direct support for both inductive computations of behaviors as well as
co-inductive specifications of properties. Two main theoretical contributions
are a Moore Family result and a parametrized worst case time complexity result.
We show that the logic and the associated solver can be used for rapid
prototyping and illustrate a wide variety of applications within Static
Analysis, Constraint Satisfaction Problems and Model Checking. In all cases the
complexity result specializes to the worst case time complexity of the
classical methods
Compilation for QCSP
We propose in this article a framework for compilation of quantified
constraint satisfaction problems (QCSP). We establish the semantics of this
formalism by an interpretation to a QCSP. We specify an algorithm to compile a
QCSP embedded into a search algorithm and based on the inductive semantics of
QCSP. We introduce an optimality property and demonstrate the optimality of the
interpretation of the compiled QCSP.Comment: Proceedings of the 13th International Colloquium on Implementation of
Constraint LOgic Programming Systems (CICLOPS 2013), Istanbul, Turkey, August
25, 201
Logic Programming Applications: What Are the Abstractions and Implementations?
This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations
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
(Co-)Inductive semantics for Constraint Handling Rules
In this paper, we address the problem of defining a fixpoint semantics for
Constraint Handling Rules (CHR) that captures the behavior of both
simplification and propagation rules in a sound and complete way with respect
to their declarative semantics. Firstly, we show that the logical reading of
states with respect to a set of simplification rules can be characterized by a
least fixpoint over the transition system generated by the abstract operational
semantics of CHR. Similarly, we demonstrate that the logical reading of states
with respect to a set of propagation rules can be characterized by a greatest
fixpoint. Then, in order to take advantage of both types of rules without
losing fixpoint characterization, we present an operational semantics with
persistent. We finally establish that this semantics can be characterized by
two nested fixpoints, and we show the resulting language is an elegant
framework to program using coinductive reasoning.Comment: 17 page
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