11th International Conference on Practical Applications of Agents and Multi-Agent Systems

Research on Agents and Multi-agent Systems has matured during the last decade and many effective applications of this technology are now deployed. PAAMS provides an international forum to presents and discuss the latest scientific developments and their effective applications, to assess the impact of the approach, and to facilitate technology transfer. PAAMS started as a local initiative, but since grown to become the international yearly platform to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to Exchange their experience in the development and deployment of Agents and Multiagents systems. PAAMS intends to bring together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on the application of multi-agent systems, to discuss and debate the major issues, and to showcase the latest systems using agent based technology. It will promote a forum for discussion on how agent based techniques, methods and tools help system designers to accomplish the mapping between available agent technology and application needs. Other stakeholders should be rewarded with a better understanding of the potential and challenges of the agent-oriented approach. This edition of PAAMS special sessions is organized by the Bioinformatics, Intelligent System and Educational Technology Research Group (http://bisite.usal.es) of the University of Salamanca. The present edition was held in Salamanca, Spain, from 22nd to 24th May 2013

Semantische Dekomposition und Marker Passing in einer künstlichen Bedeutungsrepräsentation

The research area of Distributed Artificial Intelligence aims at building intelligent agent systems. Multi-Agent Systems have been applied successfully in many domains, from an intermodal planning domain to cascading security thread simulations. But still, agents struggle with the meaning of concepts used in language. Intelligence needs language to form thoughts. Thus, the challenge addressed in this thesis is to provide a computable representation of meaning and evaluate its usefulness. Based on the theory of a mental lexicon and the thesis that meaning is a combination of symbolic and connectionist parts, I investigate the use of the theory of Natural Semantic Metalanguage (NSM) to build an artificial representation of meaning. I show that the use of NSM for creating a semantic graph out of different information sources can be utilized as a basis for Marker Passing algorithms. The Marker Passing algorithm encodes symbolic meaning to guide the reasoning over the connectionist semantic graph. Through the combination of a semantic graph and symbolic Marker Passing, I can combine connectionist and symbolic approaches to AI research to create my artificial representation of meaning. To test my approach, I build a semantic distance measure, a word sense disambiguation algorithm and a sentence similarity measure which all go head to head with the state-of-the-art. I apply those approaches to two use cases: A semantic service match marking and a context-dependent heuristics. I evaluate my heuristic by utilizing them in AI problem-solving component which uses AI planning guided by my heuristic.Die Wissenschaft im Bereich der verteilten künstlichen Intelligenz untersucht unter anderem Multi-Agenten Systeme und deren Anwendung in verschiedenen Bereichen. Solche intelligenten verteilten Systeme finden beispielsweise erfolgreich Einsatz bei der Planung intermodaler Routen oder bei der Simulation von Kaskaden Effekten durch Sicherheitsbedrohungen. Dabei entwickeln Agenten immer mehr Intelligenz zur autonomen Lösunge von neuen Problemen. Agenten kämpfen jedoch noch immer mit der Bedeutung von Konzepten der natürlichen Sprache. Intelligenz benötigt jedoch Sprache um Gedanken zu formen. Deshalb wird in dieser Arbeit die Herausforderung angegangen eine künstliche Repräsentation von Bedeutung zu erschaffen und deren Nutzbarkeit zu evaluieren. Basierend auf der Theorie eines mentalen Lexikons und darauf, dass Bedeutung aus zwei Teilen besteht (Symbolischer und Konnektivistischer Bedeutung), untersucht diese Arbeit die Verwendung der Natural Semantic Metalanguage (NSM) zum Erstellen einer künstlichen Repräsentation von Bedeutung. Hauptaugenmerk liegt dabei auf der automatischen Erzeugung eines semantischen Graphen, der durch Marker Passing Ansätze genutzt werden kann. Der semantische Graph wird dabei basierend auf der NSM Theorie aus verschiedenen Informationsquellen automatisch erstellt. Der Marker Passing Algorithmus beschreibt dabei den symbolischen Teil unseres Ansatzes. Die symbolische Information der Marker wird dazu verwendet diese geeignet über den semantischen Graphen zu verteilen. Durch die Verteilung der Marker wird eine Art von Schlussfolgerung modelliert. Durch die so entstandene Kombination aus Dekomposition und Marker Passing kann eine Mischung aus symbolische und konnektivistische Bedeutung entstehen. Die so entstandene künstliche Repräsentation von Bedeutung wird durch mehrere Experimente getestet: Ich verwende sie um ein semantisches Distanzmaß zu bauen, erstellen einen Ansatz zur Auflösung von Mehrdeutigkeit von Worten in natürlicher Sprache und erzeugen einen neuen Ansatz zur Bestimmung von Satzähnlichkeit. Dabei konnte gezeigt werden, dass die so entstandenen Ansätze dem Stand der Technik in nichts nachstehen. Des Weiteren teste ich an zwei Anwendungen ob meine künstliche Repräsentation von Bedeutung wirklich Bedeutung formalisiert: erstens anhand einer semantischen Service Matching-Komponente und zweitens einer kontextabhängigen und zielorientierten Heuristik. Diese Heuristik wird durch den Einsatz in einem Planungsalgorithmus evaluiert

Dronedarone, amiodarone and other antiarrhythmic drugs, and acute liver injuries: a case-referent study

International audienceBACKGROUND: Spontaneous reports of acute liver injuries (ALI) in patients taking dronedarone triggered an EMAalert in 2011. This study aimed to assess the risk of ALI for class III antiarrhythmic drugs controlling for the useof other potential ALI-inducing drugs.METHODS: Between 2010 and 2014, consecutive ALI cases (≥50 years-old) were identified across Germany. ALIwas defined as a new increase in at least one of the transaminases≥3 times the upper limit of normal (ULN)or≥2 ULN if alkaline phosphatase, with (“definite”case) or without (“biochemical”case) suggestive signs/symp-toms of ALI, excluding other liver diseases. Recruited community controls were matched to cases on gender, ageand inclusion date. Exposure to antiarrhythmic drugs and co-medication up to 2 years before ALI onset was in-formed by patients and confirmed by physicians' prescriptions. Adjusted Odds Ratios (aOR) were obtained fromconditional multivariable logistic regressions, adjusted for a multivariate disease risk score and co-medication.RESULTS: 252 cases and 1081 matched controls were included (59.1% females; mean age: 64 years). Exposure toclass III antiarrhythmic drugs was 4.0% in cases and 1.5% in controls, aOR = 3.6 (95% CI: 1.6–8.4). Associationswith exposure to dronedarone and amiodarone were respectively 3.1 (95% CI: 0.7–14. 8) and 5.90 (1.7–20.0).Restricting the analysis to definite or severe ALI cases did not change these results.CONCLUSIONS: Class III antiarrhythmic drugs were associated with ALI, amiodarone displaying the highest risk, andresults were robust to case definitions. Continued vigilance is needed for patients taking these drug