Author name cluster

Ren

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6 papers

1 author row

IJCAI Conference 2016 Conference Paper

Knowledge-Based Sequence Mining with ASP

Martin Gebser
Thomas Guyet
Ren
eacute; Quiniou
Javier Romero
Torsten Schaub

We introduce a framework for knowledge-based sequence mining, based on Answer Set Programming (ASP). We begin by modeling the basic task and refine it in the sequel in several ways. First, we show how easily condensed patterns can be extracted by modular extensions of the basic approach. Second, we illustrate how ASP's preference handling capacities can be exploited for mining patterns of interest. In doing so, we demonstrate the ease of incorporating knowledge into the ASP-based mining process. To assess the trade-off in effectiveness, we provide an empirical study comparing our approach with a related sequence mining mechanism.

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IJCAI Conference 2016 Conference Paper

Packing Graphs with ASP for Landscape Simulation

Thomas Guyet
Yves Moinard
Jacques Nicolas
Ren
eacute; Quiniou

This paper describes an application of Answer Set Programming (ASP) to crop allocation for generating realistic landscapes. The aim is to cover optimally a bare landscape, represented by its plot graph, with spatial patterns describing local arrangements of crops. This problem belongs to the hard class of graph packing problems and is modeled in the framework of ASP. The approach provides a compact solution to the basic problem and at the same time allows extensions such as a flexible integration of expert knowledge. Particular attention is paid to the treatment of symmetries, especially due to sub-graph isomorphism issues. Experiments were conducted on a database of simulated and real landscapes. Currently, the approach can process graphs of medium size, a size that enables studies on real agricultural practices.

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IJCAI Conference 2015 Conference Paper

Abstract Routing Models and Abstractions in the Context of Vehicle Routing

Ren
eacute; Sch
ouml; nfelder
Martin Leucker

The functional and the algebraic routing problem are generalizations of the shortest path problem. This paper shows that both problems are equivalent with respect to the concept of profile searches known from time-dependent routing. Because of this, it is possible to apply various shortest path algorithms to these routing problems. This is demonstrated using contraction hierarchies as an example. Furthermore, we show how to use Cousots’ concept of abstract interpretation on these routing problems generalizing the idea of routing approximations, which can be used to find approximative solutions and even to improve the performance of exact queries. The focus of this paper lies on vehicle routing while both the functional and algebraic routing models were introduced in the context of internet routing. Due to our formal combination of both fields, new algorithms abound for various specialized vehicle routing problems. We consider two major examples, namely the time-dependent routing problem for public transportation and the energy-efficient routing problem for electric vehicles.

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IJCAI Conference 2015 Conference Paper

H-Index Manipulation by Merging Articles: Models, Theory, and Experiments

Ren
eacute; van Bevern
Christian Komusiewicz
Rolf Niedermeier
Manuel Sorge
Toby Walsh

An author’s profile on Google Scholar consists of indexed articles and associated data, such as the number of citations and the H-index. The author is allowed to merge articles, which may affect the H-index. We analyze the parameterized complexity of maximizing the H-index using article merges. Herein, to model realistic manipulation scenarios, we define a compatability graph whose edges correspond to plausible merges. Moreover, we consider multiple possible measures for computing the citation count of a merged article. For the measure used by Google Scholar, we give an algorithm that maximizes the H-index in linear time if the compatibility graph has constant-size connected components. In contrast, if we allow to merge arbitrary articles, then already increasing the H-index by one is NP-hard. Experiments on Google Scholar profiles of AI researchers show that the H-index can be manipulated substantially only by merging articles with highly dissimilar titles, which would be easy to discover.

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AAMAS Conference 2012 Conference Paper

Agent Based Monitoring of Gestational Diabetes Mellitus

Ren
eacute; Schumann
Stefano Bromuri
Johannes Krampf
Michael Schumacher

Gestational diabetes is a type of diabetes affecting temporarily some otherwise healthy pregnant women. Current medical practices do not allow the doctors to monitor such patients as closely as needed. Pervasive Health is a discipline requiring distributed ICT infrastructures to help bridging the gap between the patients and the doctors. In this demo paper we present a complete information system for patient monitoring, including mobile devices for acquiring data from patients and a Web interface for doctors to check the status of their patients. At the core of this information system a multi-agent system monitors the patient health state and triggers alerts to the doctor to raise attention on the specific conditions of a patient. This allows the doctor to react faster to changes of condition of the woman, benefiting the baby's and the mother's health.

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IJCAI Conference 2011 Conference Paper

Extracting Temporal Patterns from Interval-Based Sequences

Thomas Guyet
Ren
eacute; Quiniou

Most of the sequential patterns extraction methods proposed so far deal with patterns composed of events linked by temporal relationships based on simple precedence between instants. In many real situations, some quantitative information about event duration or inter-event delay is necessary to discriminate phenomena. We propose the algorithm QTIPrefixSpan for extracting temporal patterns composed of events to which temporal intervals describing their position in time and their duration are associated. It extends algorithm PrefixSpan with a multi-dimensional interval clustering step for extracting the representative temporal intervals associated to events in patterns. Experiments on simulated data show that our algorithm is efficient for extracting precise patterns even in noisy contexts and that it improves the performance of a former algorithm which used a clustering method based on the EM algorithm.

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