Progressive automation to gain appropriate trust in management automation systems

This document summarizes the results of the Working Group 5 - ``Progressive Automation / Trust\u27\u27 - at the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems\u27\u27 (organized by A. Andrzejak, K. Geihs, O. Shehory and J. Wilkes). The seminar was held from May 10th 2009 to May 15th 2009 in Schloss Dagstuhl~--~Leibniz Center for Informatics

Une approche multi-agent pour les algorithmes génétiques coévolutionnaires hybrides et dynamiques : modèle d'organisation multi-agent et mise en oeuvre sur des problèmes métiers

In this dissertation we assert that modeling Coevolutionary Genetic Algorithms (CGAs) as organizational multi-agent systems overcomes the lack of explicitness at the level of the algorithms structure, interactions and adaptation to existing models and platforms. We therefore introduce MAS4EVO, Multi-Agent Systems for EVolutionary Optimization, a new agent (re-)organizational model based on Moise+ and dedicated to evolutionary optimization. This model was used to describe existing CGAs as well as to build two new variants, hybrid and dynamic, of a competitive CGA. MAS4EVO is implemented in DAFO (Distributed Agent Framework for Optimization) which permits the use, the manipulation and the distribution of these CGAs, on hard optimization problems. The CGAs experimentations were conducted on two business problems, the first one being an inventory management problem and the second one being a new topology control problem in wireless ad hoc networks.Nous défendons la thèse selon laquelle la modélisation des Algorithmes Génétiques Coévolutionnaires (AGCs) sous forme de systèmes multi-agent organisationnels répond au manque d'expressivité en termes de structure, d'interactions et d'adaptation de ces algorithmes dans les modèles et plateformes existants. Dans cette optique nous introduisons MAS4EVO, Multi-Agent Systems for EVolutionary Optimization, un nouveau modèle agent (re-)organisationnel basé sur Moise+. MAS4EVO est implémenté dans DAFO (Distributed Agent Framework for Optimization), un framework multi-agent organisationnel permettant l'utilisation, la manipulation et la distribution d'AGCs existants et nouvellement créés (hybride et dynamique) pour l'optimisation de problèmes difficiles. Les expérimentations de ces AGCs ont été conduites sur deux problèmes d'optimisation métier, le premier étant un problème de gestion de stock et le second étant un problème de contrôle de topologie dans les réseaux ad hoc sans fil

Optimization and Learning

International audienc

Multi-objective evolutionary approach for the satellite payload power optimization problem

International audienceToday’s world is a vast network of global communicationssystems in which satellites provide high-performanceand long distance communications. Satellites are able to forwardsignals after amplification to offer a high level of serviceto customers. These signals are composed of many differentchannel frequencies continuously carrying real-time data feeds.Nevertheless, the increasing demands of the market force satelliteoperators to develop efficient approaches to manage satellite configurations,in which power transmission is one crucial criterion.Not only the signal power sent to the satellite needs to be optimalto avoid large costs but also the power of the downlink signalhas to be strong enough to ensure the quality of service. In thiswork, we tackle for the first time the bi-objective input/outputpower problem with multi-objective evolutionary algorithms todiscover efficient solutions. A problem specific indirect encodingis proposed and the performance of three state-of-the-art multiobjectiveevolutionary algorithms, i.e. NSGA-II, SPEA2 andMOCell, is compared on real satellite payload instances

A Generative Hyper-Heuristic based on Multi-Objective Reinforcement Learning: the UAV Swarm Use Case

International audienc

The Comparison Of Reliability Of The Generalizability Theory And The Test-Retest Technique For The Short Answered Maths Exam

Bu çalışmada ilköğretim 7.sınıf öğrencilerine yönelik olarak hazırlanmış Kısa Cevaplı Matematik Yazılı Sınavından' elde edilen ölçümlerin güvenirliğinin incelenmesi amaçlanmıştır. Bu amaçla yazılı sınavın üç bağımsız puanlayıcı tarafından puanlanmasıyla elde edilen ölçümler Klasik Test Kuramının Test Tekrar Test Yöntemi ve Genellenebilirlik (G) Kuramının çaprazlanmış deseniyle kıyaslanmıştır. Her iki kuramda yapılan güvenirlik analizleri sonucu elde edilen güvenirlik katsayıları karşılaştırılarak aralarında manidar bir farklılık olup olmadığı test edilmiştir. Araştırmanın çalışma grubunu, Çorum ilinde 2014-2015 eğitim öğretim yılında yedinci sınıfta eğitim görmekte olan 99 öğrenci oluşturmuştur. Öğrencilere Kısa Cevaplı Matematik Yazılı Sınavı' uygulanmış ve öğrenci cevapları 3 bağımsız puanlayıcı tarafından cevap anahtarı ile puanlanmıştır. Genellenebilirlik Kuramında birey (b), madde (m) ve puanlayıcı (p) değişkenleri olmak üzere her bireyin her maddeye ulaştığı ve puanlayıcıların her biri tarafından puanlandığı b x m x p çaprazlanmış desen kullanılmıştır. Klasik Test Kuramında ise Test Tekrar Test yöntemi kullanılmıştır. Kısa Cevaplı Matematik Yazılı Sınavı iki hafta arayla aynı öğrencilere uygulanmış ve aynı puanlayıcı tarafından puanlanmıştır. Araştırma sonucunda her iki kuramdan elde edilen güvenirlik katsayıları kıyaslandığında aralarında manidar bir farklılık olmadığı gözlenmiştir. Dolayısıyla her iki kuramda benzer sonuçlar vermiştir.In this study, it was aimed to evaluate the reliability of testing obtained from the Short Answer Maths Examination prepared for elementary school grade 7 students. For his purpose, the measurements obtained by the written exam scored by three in dependent raters were compared with the Test-Retest method of Classical Test Theory and crossed pattern of Generalizability (G) Theory. It was tested whether there is meaningful difference between both theories by comparing the reliability coefficients obtained as a result of reliability analysis. 99 students of 7th grade who studies in school in Çorum during 2014-2015 education year, consist the study group of this research. Students were applied Short Answer Mathematics Examination and students responses were scored by 3 independent scores with an answer key. In Generalizability Theory p x i x r crossed pattern in which each person could reach each item including person (p), item (i) and raters (r) and scored by each raters. In the Classical Test Theory Test-Retest method was used. Short Answer Mathematics Examination was applied to the same group of student in two week time interval and obtained measurement results were scored by the same raters. When compared to the results of two theories, it is observed that there is no differences between the reliability coefficient

Multi-objective evolutionary approach for the satellite payload power optimization problem

International audienceToday’s world is a vast network of global communications systems in which satellites provide high-performance and long distance communications. Satellites are able to forward signals after amplification to offer a high level of service to customers. These signals are composed of many differentchannel frequencies continuously carrying real-time data feeds. Nevertheless, the increasing demands of the market force satellite operators to develop efficient approaches to manage satellite configurations, in which power transmission is one crucial criterion. Not only the signal power sent to the satellite needs to be optimal to avoid large costs but also the power of the downlink signal has to be strong enough to ensure the quality of service. In this work, we tackle for the first time the bi-objective input/output power problem with multi-objective evolutionary algorithms to discover efficient solutions. A problem specific indirect encoding is proposed and the performance of three state-of-the-art multiobjective evolutionary algorithms, i.e. NSGA-II, SPEA2 and MOCell, is compared on real satellite payload instances

Clustering approaches for visual knowledge exploration in molecular interaction networks.

BACKGROUND: Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually. RESULTS: We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps. CONCLUSIONS: In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration