1,881 research outputs found
Exploiting Term Hiding to Reduce Run-time Checking Overhead
One of the most attractive features of untyped languages is the flexibility
in term creation and manipulation. However, with such power comes the
responsibility of ensuring the correctness of these operations. A solution is
adding run-time checks to the program via assertions, but this can introduce
overheads that are in many cases impractical. While static analysis can greatly
reduce such overheads, the gains depend strongly on the quality of the
information inferred. Reusable libraries, i.e., library modules that are
pre-compiled independently of the client, pose special challenges in this
context. We propose a technique which takes advantage of module systems which
can hide a selected set of functor symbols to significantly enrich the shape
information that can be inferred for reusable libraries, as well as an improved
run-time checking approach that leverages the proposed mechanisms to achieve
large reductions in overhead, closer to those of static languages, even in the
reusable-library context. While the approach is general and system-independent,
we present it for concreteness in the context of the Ciao assertion language
and combined static/dynamic checking framework. Our method maintains the full
expressiveness of the assertion language in this context. In contrast to other
approaches it does not introduce the need to switch the language to a (static)
type system, which is known to change the semantics in languages like Prolog.
We also study the approach experimentally and evaluate the overhead reduction
achieved in the run-time checks.Comment: 26 pages, 10 figures, 2 tables; an extension of the paper version
accepted to PADL'18 (includes proofs, extra figures and examples omitted due
to space reasons
On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of
probabilistic logic learning and statistical relational learning. In this
endeavor, many probabilistic logics have been developed. ProbLog is a recent
probabilistic extension of Prolog motivated by the mining of large biological
networks. In ProbLog, facts can be labeled with probabilities. These facts are
treated as mutually independent random variables that indicate whether these
facts belong to a randomly sampled program. Different kinds of queries can be
posed to ProbLog programs. We introduce algorithms that allow the efficient
execution of these queries, discuss their implementation on top of the
YAP-Prolog system, and evaluate their performance in the context of large
networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming
(TPLP
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
Efficient Groundness Analysis in Prolog
Boolean functions can be used to express the groundness of, and trace
grounding dependencies between, program variables in (constraint) logic
programs. In this paper, a variety of issues pertaining to the efficient Prolog
implementation of groundness analysis are investigated, focusing on the domain
of definite Boolean functions, Def. The systematic design of the representation
of an abstract domain is discussed in relation to its impact on the algorithmic
complexity of the domain operations; the most frequently called operations
should be the most lightweight. This methodology is applied to Def, resulting
in a new representation, together with new algorithms for its domain operations
utilising previously unexploited properties of Def -- for instance,
quadratic-time entailment checking. The iteration strategy driving the analysis
is also discussed and a simple, but very effective, optimisation of induced
magic is described. The analysis can be implemented straightforwardly in Prolog
and the use of a non-ground representation results in an efficient, scalable
tool which does not require widening to be invoked, even on the largest
benchmarks. An extensive experimental evaluation is givenComment: 31 pages To appear in Theory and Practice of Logic Programmin
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