37 research outputs found
Multi-Shot Stream Reasoning in Answer Set Programming: A Preliminary Report
In the past, we presented a first approach for stream reasoning using Answer Set Programming (ASP). At the time, we implemented an exhaustive wrapper for our underlying ASP system, clingo, to enable reasoning over continuous data streams. Nowadays, clingo natively supports multi-shot solving: a technique for processing continuously changing logic programs. In the context of stream reasoning, this allows us to directly implement seamless sliding-window-based reasoning over emerging data. In this paper, we hence present an exhaustive update to our stream reasoning approach that leverages multi-shot solving. We describe the implementation of the stream reasoner's architecture, and illustrate its workflow via job shop scheduling as a running example
A Machine Learning guided Rewriting Approach for ASP Logic Programs
Answer Set Programming (ASP) is a declarative logic formalism that allows to
encode computational problems via logic programs. Despite the declarative
nature of the formalism, some advanced expertise is required, in general, for
designing an ASP encoding that can be efficiently evaluated by an actual ASP
system. A common way for trying to reduce the burden of manually tweaking an
ASP program consists in automatically rewriting the input encoding according to
suitable techniques, for producing alternative, yet semantically equivalent,
ASP programs. However, rewriting does not always grant benefits in terms of
performance; hence, proper means are needed for predicting their effects with
this respect. In this paper we describe an approach based on Machine Learning
(ML) to automatically decide whether to rewrite. In particular, given an ASP
program and a set of input facts, our approach chooses whether and how to
rewrite input rules based on a set of features measuring their structural
properties and domain information. To this end, a Multilayer Perceptrons model
has then been trained to guide the ASP grounder I-DLV on rewriting input rules.
We report and discuss the results of an experimental evaluation over a
prototypical implementation.Comment: In Proceedings ICLP 2020, arXiv:2009.0915
Combinatorial Methods in Grid based Meshing
This paper describes a novel method of generating hex-dominant meshes using
pre-computed optimal subdivisions of the unit cube in a grid-based approach.
Our method addresses geometries that are standard in mechanical engineering and
often must comply with the restrictions of subtractive manufacturability. A
central component of our method is the set of subdivisions we pre-compute with
Answer Set Programming. Despite being computationally expensive, we obtain
optimal meshes of up to 35 nodes available to our method in a template fashion.
The first step in our grid-based method generates a coarse Precursor Mesh for
meshing complete parts representing the bar stock. Then, the resulting mesh is
generated in a subtractive manner by inserting and fitting the pre-generated
subdivisions into the Precursor Mesh. This step guarantees that the elements
are of good quality. In the final stage, the mesh nodes are mapped to geometric
entities of the target geometry to get an exact match. We demonstrate our
method with multiple examples showing the strength of this approach