2,537 research outputs found
Variable Ranges in Linear Constraints
We introduce an extension of linear constraints, called linearrange constraints, which allows for (meta-)reasoning about the approximation width of variables. Semantics for linearrange constraints is provided in terms of parameterized linear systems. We devise procedures for checking satisfiability and for entailing the maximal width of a variable. An extension of the constraint logic programming language CLP(R) is proposed by admitting linear-range constraints
Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison
Many logic programming based approaches can be used to describe and solve
combinatorial search problems. On the one hand there is constraint logic
programming which computes a solution as an answer substitution to a query
containing the variables of the constraint satisfaction problem. On the other
hand there are systems based on stable model semantics, abductive systems, and
first order logic model generators which compute solutions as models of some
theory. This paper compares these different approaches from the point of view
of knowledge representation (how declarative are the programs) and from the
point of view of performance (how good are they at solving typical problems).Comment: 15 pages, 3 eps-figure
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
Tools for Search Tree Visualization: The APT Tool
The control part of the execution of a constraint logic program can be conceptually shown as a search-tree, where nodes correspond to calis, and whose branches represent conjunctions and disjunctions. This tree represents the search space traversed by the program, and has also a direct
relationship with the amount of work performed by the program. The nodes of the tree can be used to display information regarding the state and origin of instantiation of the variables involved in each cali. This depiction can also be used for the enumeration process. These are the features implemented in APT, a tool which runs constraint logic programs while depicting a (modified) search-tree, keeping at the same time information about the state of the variables at every moment in the execution. This information can be used to replay the execution at will, both forwards and backwards in time. These views can be abstracted when the size of the execution requires it. The search-tree view is used as a framework onto which constraint-level visualizations (such as those presented in the following chapter) can be attached
SLDNFA-system
The SLDNFA-system results from the LP+ project at the K.U.Leuven, which
investigates logics and proof procedures for these logics for declarative
knowledge representation. Within this project inductive definition logic
(ID-logic) is used as representation logic. Different solvers are being
developed for this logic and one of these is SLDNFA. A prototype of the system
is available and used for investigating how to solve efficiently problems
represented in ID-logic.Comment: 6 pages conference:NMR2000, special track on System descriptions and
demonstratio
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