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Liste der Ergebnisse

Aktivitaet2127 + (Mitglied des Programmkomitees des ICLP/LPNMR Doctoral Consortiums (ICLP/LPNMR-DC 2024))
Aktivitaet2040 + (Mitglied des Programmkomitees des Knowledge Representation and Reasoning (KRR) tracks der 18. EPIA - Portuguese Conference on Artificial Intelligence (EPIA 2017))
Aktivitaet2018 + (Mitglied des Programmkomitees des Pragmatics of SAT Workshops 2014)
Aktivitaet2053 + (Mitglied des Programmkomitees des Reasoning on Data Workshop at the Web Conference 2018)
Aktivitaet2039 + (Mitglied des Programmkomitees des Special Tracks on Applications of Argumentation (APPARG 2017) bei der 30. International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2017))
Aktivitaet2067 + (Mitglied des Programmkomitees des Workshops on “Large-Scale Data Management and Processing – Applications in Research and Industry” 2020 (FGDB@LWDA 2020)LWDA 2020 ()
Aktivitaet2093 + (Mitglied des Programmkomitees des dritten International Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2022))
Aktivitaet2071 + (Mitglied des Programmkomitees des ersten International Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2020))
Aktivitaet2038 + (Mitglied des Programmkomitees des internationalen Workshops on Trends and Applications of Answer Set Programming (TAASP 2016))
Aktivitaet2034 + (Mitglied des Programmkomitees des internationalen Workshops on Systems and Algorithms for Formal Argumentation (SAFA 2016))
Aktivitaet2079 + (Mitglied des Programmkomitees des zweiten International Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2021))
Aktivitaet2008 + (Mitglied des Senior Member Presentation Track Programmkomitees der 29. AAAI Conference on Artificial Intelligence (AAAI 2015))
Aktivitaet2078 + (Mitglied im Editorial Board des Argument & Computation Journals)
Aktivitaet2084 + (Mitglied im Fakultätsrat Informatik, TU Dresden (2022-2027))
Aktivitaet2000 + (Mitglied im Programmkomitee der 23. ACM International Conference on Information and Knowledge Management (CIKM 2014))
Aktivitaet2006 + (Mitglied im Programmkomitee der 31. International Conference on Logic Programming (ICLP 2015))
Aktivitaet2007 + (Mitglied im Programmkomitee der 7. International Conference on Agents and Artificial Intelligence (ICAART 2015))
Aktivitaet2023 + (Mitglied im Programmkomitee der 8. International Conference on Agents and Artificial Intelligence (ICAART 2016))
Aktivitaet2005 + (Mitglied im Programmkomitee der 9th International Web Rule Symposium (RuleML 2015))
Aktivitaet2080 + (Mitglied im Programmkomitee der AJCAI 2021)
Aktivitaet2115 + (Mitglied im Programmkomitee der ECAI 2023)
Aktivitaet2121 + (Mitglied im Programmkomitee der ECAI 2024)
Aktivitaet2107 + (Mitglied im Programmkomitee der IJCAI 2023)
Aktivitaet2122 + (Mitglied im Programmkomitee der IJCAI 2024)
Aktivitaet2125 + (Mitglied im Programmkomitee der KR 2024)

Aktivitaet2102 + (Mitglied im Programmkomitee von AJCAI 2022)

Aktivitaet2083 + (Mitglied im Programmkomitee von IJCAI-ECAI 2022)
Aktivitaet2101 + (Mitglied im Programmkomitee von KR 2022)
Aktivitaet2085 + (Mitglied im Prüfungsausschuss Bachelor Informatik)
Aktivitaet2100 + (Mitglied im Senior-Programmkomitee der AAAI 2023)
Aktivitaet2132 + (Mitglied im Senior-Programmkomitee der AAAI 2024)
Aktivitaet2133 + (Mitglied im Senior-Programmkomitee der AAAI 2025)
Aktivitaet2090 + (Mitglied im Steering Committee der International Conference on Web Reasoning and Rule Systems (RR))
Aktivitaet2025 + (Mitglied im Steering Committee von CADE: Conference on Automated Deduction (2002-2009, President 2004-2009; 2010-2013, President 2011-2013))
Aktivitaet2028 + (Mitglied im Steering Committee von DL: Workshop on Description Logics (2000-2002; 2008-2011))
Modal- und Temporallogiken + (Modal- und Temporallogiken sind logische F … Modal- und Temporallogiken sind logische Formalismen, die auf einer Semantik möglicher Welten basieren. Sie sind dafür geeignet, Phänomene wie das Verhalten von Computerprogrammen, Softwaresysteme, ethische Normen, Wissen und von Agenten ausgeführte Handlungen zu repräsentieren sowie Schlüsse über diese Phänomene zu modellieren. Kanonische Beispiele für solche Logiken sind die grundlegende Modallogik K, die lineare temporale Logik (LTL), Temporallogiken mit verzweigender Zeitstruktur wie CTL oder CTL*, die Dynamische Epistemische Logik sowie STIT-Logiken. Die diesbezügliche Forschung umfasst das präzise Bestimmen der Komplexitätseigenschaften typischer Entscheidungsprobleme und das Entwickeln von effizienten Beweiskalkülen.Entwickeln von effizienten Beweiskalkülen.)
A Glimpse into Propositional Model Counting + (Model counting (#SAT) asks to compute the … Model counting (#SAT) asks to compute the number of satisfying assignments for a propositional formula. The decision version (SAT) received widespread interest in computational complexity, formed many applications in modern combinatorial problem solving, and can be solved effectively for millions of variables on structured instances. #SAT is much harder than SAT and requires more elaborate solving techniques. In this talk, we revisit the problem, its complexity, and explain its connection to quantitative AI. We briefly overview solving techniques and illustrate a parameterized algorithm and implementation to tackle the problem. While purely parameterized approaches from theory often suffer practical limitations, we elaborate that a parameterized algorithm can be successful when combining it with modern hardware that takes advantage of parallelism.dware that takes advantage of parallelism.)
From Data to Knowledge: Extending Database Techniques for Knowledge Graphs + (Modern applications like recommender syste … Modern applications like recommender systems and question answering systems can leverage models beyond traditional data representations. These novel applications build upon knowledge, which cannot be easily captured with relational data models used in databases. Instead, Knowledge Graphs (KGs) allow for modeling, in a semi-structured way, inter-connected facts or statements annotated with semantics. In KGs, concepts and entities correspond to nodes while their connections are modeled as directed and labeled edges, creating a graph.While the models for representing relational data and KGs differ considerably, the architecture for querying databases have served as a foundation for querying KGs. However, not all the advancements in databases can be directly applied to KGs. This lecture will present some necessary extensions as well as successful applications of database techniques to efficiently execute queries over KGs. First, I will introduce the problem of query optimization and present extensions to traditional optimizers to cope with the semi- structured nature of KGs. Then, I will present the application of adaptive execution techniques to handle unexpected conditions when querying decentralized KGs. I will conclude with an outlook on future research directions, which include preliminary results on applying Deep Learning to the problem of query optimization for KGs.Maribel Acosta studied Computer Science at the Universidad Simon Bolivar in Venezuela, where she started working on Heterogeneous Databases and the Semantic Web. In 2017, she finished her PhD in the Knowledge Management group at the Karlsruhe Institute of Technology (KIT) on the topic "Query Processing over Graph-structured Data on the Web". After that, she was a postdoc and a lecturer (Akademische Rätin) at the Web Science group also at KIT. She has participated as a Track Chair of several international Semantic Web conferences and also as a reviewer in international conferences on the Web (WWW) and Artificial Intelligence (AAAI, NEURIPS). Since October 2020, Maribel is an Assistant Professor at the Ruhr University Bochum, where she will hold lectures about Databases, Information Systems, and Knowledge Graphs. This talk will take place online via BigBlueButton. To access the room, take one of the following links: with ZIH-login:https://selfservice.zih.tu-dresden.de/l/link.php?m=94316&p=76ddab55without ZIH-login:https://selfservice.zih.tu-dresden.de/link.php?m=94316&p=7b2c73ef.zih.tu-dresden.de/link.php?m=94316&p=7b2c73ef)
Verbinden von Suchabstraktionen zur echten Suche + (Moderne SAT Solver sind sehr komplexe Syst … Moderne SAT Solver sind sehr komplexe Systeme mit vielen integrierten Techniken und Heuristiken. Um diese intuitiv zu verstehen wurde eine Abstraktion entwickelt: die Suche eines Ausgangs in einem Labyrinth. Zu den verschiedenen Situationen im Labyrinth sollen entsprechende Formeln gefunden werden, sodass sich der SAT Solver bei der Suche genau so verhält wie der Agent im Labyrinth.nau so verhält wie der Agent im Labyrinth.)
Approximation Fixpoint Theory – A Unifying Framework for Non-monotonic Semantics + (Motivated by structural similarities in th … Motivated by structural similarities in the semantics of Reiter'sdefault logic and the stable model semantics of logic programming (amongothers), Denecker, Marek, and Truszczyński set out to isolate thesesimilarities in a purely algebraic setting. The result is now known asapproximation fixpoint theory, and allows to study the semantics of themajor non-monotonic knowledge representation formalisms in an abstract,uniform framework.After briefly recalling some lattice theory, we will present the mainconcepts of approximation fixpoint theory, applying it to the case oflogic programming as a running example.of logic programming as a running example.)
Approximation Fixpoint Theory – A Unifying Framework for Non-monotonic Semantics Part 2 + (Motivated by structural similarities in th … Motivated by structural similarities in the semantics of Reiter'sdefault logic and the stable model semantics of logic programming (among others), Denecker, Marek, and Truszczyński set out to isolate these similarities in a purely algebraic setting. The result is now known as approximation fixpoint theory, and allows to study the semantics of the major non-monotonic knowledge representation formalisms in an abstract, uniform framework. After briefly recalling some lattice theory, we will present the main concepts of approximation fixpoint theory, applying it to the case oflogic programming as a running example.This is the second part of the talk,Link: https://bbb.tu-dresden.de/b/ali-zgz-l8d-52nttps://bbb.tu-dresden.de/b/ali-zgz-l8d-52n)
9th International Workshop on Computational Logic in Multi-Agent Systems (CLIMA) + (Multi-Agent Systems are communities of pro … Multi-Agent Systems are communities of problem-solving entities that can perceive and act upon their environment in order to achieve both their individual goals and their joint goals. The work on such systems integrates many technologies and concepts from artificial intelligence and other areas of computing as well as other disciplines. Over recent years, the agent paradigm gained popularity, due to its applicability to a full spectrum of domains, such as search engines, recommendation systems, educational support, e-procurement, simulation and routing, electronic commerce and trade, etc. Computational logic provides a well-defined, general, and rigorous framework for studying the syntax, semantics and procedures for the various tasks in individual agents, as well as the interaction between, and integration amongst, agents in multi-agent systems. It also provides tools, techniques and standards for implementations and environments, for linking specifications to implementations, and for the verification of properties of individual agents, multi-agent systems and their implementations.i-agent systems and their implementations.)
Multiagentensysteme + (Multiagentensysteme (MAS) befassen sich mi … Multiagentensysteme (MAS) befassen sich mit der Interaktion autonomer Agenten, die in der Lage sind, unabhängig Entscheidungen zu treffen und in einer gemeinsamen Umgebung agieren. Die MAS-Forschung untersucht, wie Agenten kooperieren, konkurrieren oder ihre Handlungen koordinieren können, um individuelle oder kollektive Ziele zu erreichen. Die theoretischen Grundlagen dieses Feldes stammen aus Disziplinen wie der Spieltheorie, Sozialwahltheorie, verteiltem Rechnen und allgemeiner der Künstlichen Intelligenz. Ein zentrales Anliegen in MAS ist die Gestaltung von Mechanismen, die eine effiziente Zusammenarbeit oder Konkurrenz zwischen den Agenten ermöglichen, insbesondere wenn die Agenten auf der Grundlage klar formulierter Ziele handeln. In unserer Forschung gehen wir häufig davon aus, dass die Agenten mit einer Reihe von möglichen Optionen konfrontiert sind. Obwohl alle Agenten diese Optionen kennen, wissen sie nicht, welche dieser Optionen die richtige oder überlegene Wahl darstellt – die sogenannte „Ground Truth“. In solchen Systemen arbeiten die Agenten zusammen, um diese Ground Truth zu identifizieren, die ihnen zu Beginn noch unbekannt ist.n, die ihnen zu Beginn noch unbekannt ist.)
Explaining neural network reasoning + (Neural networks are in heavy use everywher … Neural networks are in heavy use everywhere in artificial intelligence today. They produce state of the art results in many different domains. Still the lack of comprehensibility of neural networks makes them difficult to use in certain domains where human understandable explanations are needed. In this talk we present different approaches on how to automatically generate explanations of the neural networks reasoning.anations of the neural networks reasoning.)
Award1024 + (Norbert Manthey, Tobias Philipp and Christ … Norbert Manthey, Tobias Philipp and Christoph Wernhard received the SAT 2013 Best Paper Award for their paper "Soundness of Inprocessing in Clause Sharing SAT Solvers", which was presented at the SAT, the leading international conference on theory and applications of satisfiability testing.For more information, please click the [http://sat2013.cs.helsinki.fi/ conference web page], the [http://link.springer.com/book/10.1007/978-3-642-39071-5/page/1 proceedings web page] or the [http://link.springer.com/chapter/10.1007/978-3-642-39071-5_4 paper].hapter/10.1007/978-3-642-39071-5_4 paper].)
Notation3 Logic: From informal to formal semantics + (Notation3 Logic is a rule-based extension … Notation3 Logic is a rule-based extension of RDF. Since its invention, the logic has been refined and applied in several reasoning engines like for example EYE, Cwm and FuXi. But despite these developments, a clear formal definition of Notation3’s semantics is still missing and the details of the logic are only defined in an informal way. This lack of formalisation does not only cause theoretical problems - the relationship to other logics cannot be investigated - it also has practical consequences: in many cases the interpretations of the same formula differ between reasoning engines. In this talk, I will explain these differences and discuss how the formal semantics of the logic can be defined based on the informal specifications and the implementations.This talk will be held online. If there is any interest in attending, please send an e-mail to thomas.feller@tu-dresden.de. an e-mail to thomas.feller@tu-dresden.de.)
Enabling Fine-grained RDF Data Completeness Assessment + (Nowadays, more and more RDF data is becomi … Nowadays, more and more RDF data is becoming available on the Semantic Web. While the Semantic Web is generally incomplete by nature, on certain topics, it already contains complete information and thus, queries may return all answers that exist in reality. We develop a technique to check query completeness based on RDF data annotated with completeness information, taking into account data-specific inferences that lead to an inference problem which is \Pi^P_2-complete. We then identify a practically relevant fragment of completeness information, suitable for crowdsourced, entity-centric RDF data sources such as Wikidata, for which we develop an indexing technique that allows to scale completeness reasoning to Wikidata-scale data sources. We verify the applicability of our framework using Wikidata and develop COOL-WD, a completeness tool for Wikidata, used to annotate Wikidata with completeness statements and reason about the completeness of query answers over Wikidata. The tool is available [http://cool-wd.inf.unibz.it/ here].lable [http://cool-wd.inf.unibz.it/ here].)
Context Reasoning for Role-Based Models + (Nowadays, we are literally everywhere surr … Nowadays, we are literally everywhere surrounded by software systems. These should cope with very complex scenarios including the ability of context-awareness and self-adaptability. The concept of roles provide the means to model such complex, context-dependent systems. In role-based systems, the relational and context-dependent properties of objects are transferred into the roles that the object plays in a certain context. However, even if the domain can be expressed in a well-structured and modular way, role-based models can still be hard to comprehend due to the sophisticated semantics of roles, contexts and different constraints. Hence, unintended implications or inconsistencies may be overlooked. A feasible logical formalism is required here. In this setting Description Logics (DLs) fit very well as a starting point for further considerations since as a decidable fragment of first-order logic they have both an underlying formal semantics and decidable reasoning problems. DLs are a well-understood family of knowledge representation formalisms which allow to represent application domains in a well-structured way by DL-concepts, i.e. unary predicates, and DL-roles, i.e. binary predicates. However, classical DLs lack expressive power to formalise contextual knowledge which is crucial for formalising role-based systems. We investigate a novel family of contextualised description logics that is capable of expressing contextual knowledge and preserves decidability even in the presence of rigid DL-roles, i.e. relational structures that are context-independent. For these contextualised description logics we thoroughly analyse the complexity of the consistency problem. Furthermore, we present a mapping algorithm that allows for an automated translation from a formal role-based model, namely a Compartment Role Object Model (CROM), into a contextualised DL ontology. We prove the semantical correctness and provide ideas how features extending CROM can be expressed in our contextualised DLs. As final step for a completely automated analysis of role-based models, we investigate a practical reasoning algorithm and implement the first reasoner that can process contextual ontologies.er that can process contextual ontologies.)
Pattern-based ontology modeling and some of its implications for Description Logics research + (One of the original motivations for develo … One of the original motivations for developing ontologies was that they were to act as generic domain models which can be easily reused and repurposed. However, ontology modeling for applications in practice is often driven by very concrete use cases, and thus the corresponding ontologies are often strongly tailored towards meeting very specific use case requirements. As a consequence, ontologies in practice are often not easy to repurpose. In this presentation, we discuss how to model ontologies in such a way as to simplify future reuse. In particular, we will discuss modularization of ontologies, the role of ontology design patterns, and ontology views. Our observations furthermore expose limitations of current description logics which may stimulate research investigations.Pascal Hitzler is (full) Professor and Director of Data Science at the Department of Computer Science and Engineering at Wright State University in Dayton, Ohio, U.S.A. His research record lists over 300 publications in such diverse areas as semantic web, neural-symbolic integration, knowledge representation and reasoning, machine learning, denotational semantics, and set-theoretic topology. He is Editor-in-chief of the Semantic Web journal by IOS Press, and of the IOS Press book series Studies on the Semantic Web. He is co-author of the W3C Recommendation OWL 2 Primer, and of the book Foundations of Semantic Web Technologies by CRC Press, 2010 which was named as one out of seven Outstanding Academic Titles 2010 in Information and Computer Science by the American Library Association's Choice Magazine, and has translations into German and Chinese. He is on the editorial board of several journals and book series and is a founding steering committee member of the Web Reasoning and Rule Systems (RR) conference series, of the Neural-Symbolic Learning and Reasoning (NeSy) workshop series, and of the Association for Ontology Design and Patterns (ODPA). He also frequently acts as conference chair in various functions. as conference chair in various functions.)
Explaining Answer Sets using Argumentation Theory + (One of the prominent techniques for solvin … One of the prominent techniques for solving knowledge representation and reasoning problems is answer set programming (ASP). A problem is encoded as a set of inference rules expressing everything known about this problem, and the problem's solutions, the answer sets, are the sets of all non-conflicting literals deducible from these rules. Answer sets can be efficiently computed using answer set solvers; however they do not provide any explanation as to why a literal is or is not part of this answer set. Having an explanation of literals in a solution is particularly desirable when ASP is used as a reasoning tool in applications such as medical decision making, where the solutions are used by non-ASP-experts like doctors.In this talk I will present ABA-Based Answer Set Justifications, which based on Argumentation Theory explain why a literal is or is not contained in an answer set. Reasoning in Argumentation Theory involves forming arguments from the given knowledge and evaluating the conflicts between them. Since this type of reasoning is easily understandable for humans yet processable by computers, Argumentation Theory is a suitable technique for providing explanations of answer sets for humans. ABA-Based Answer Set Justifications are based on the correspondence between answer sets and stable extensions in Argumentation Theory and provide an explanation in terms of an admissible fragment of the stable extension corresponding to the answer set in question.rresponding to the answer set in question.)
Standpoint Logic: Multi-Perspective Knowledge Representation + (Ontologies and knowledge bases encode, to … Ontologies and knowledge bases encode, to a certain extent, the stand-points or perspectives of their creators. As differences and conflicts between stand-points should be expected in multi-agent scenarios, this will pose challenges for shared creation and usage of knowledge sources.Our work pursues the idea that, in some cases, a framework that can handle diverse and possibly conflicting standpoints is more useful and versatile than forcing their unification, and avoids common compromises required for their merge. Moreover, in analogy to the notion of family resemblance concepts, we propose that a collection of standpoints can provide a simpler yet more faithful and nuanced representation of some domains.To this end, we present standpoint logic, a multi-modal framework that is suitable for expressing information with semantically heterogeneous vocabularies, where a standpoint is a partial and acceptable interpretation of the domain. Standpoints can be organised hierarchically and combined, and complex correspondences can be established between them. We provide a formal syntax and semantics, outline the complexity for the propositional case, and explore the representational capacities of the framework in relation to standard techniques in ontology integration, with some examples in the Bio-Ontology domain.This is a test talk for a presentation at FOIS 2021. Thus it will have a duration of 15 minutes after which questions can be asked. The talk will be given online via BigBlueButton. To access the room, use one of the following links:with ZIH-login:https://selfservice.zih.tu-dresden.de/l/link.php?m=145027&p=4d1d790dwithout ZIH-login:https://selfservice.zih.tu-dresden.de/link.php?m=145027&p=93e20500zih.tu-dresden.de/link.php?m=145027&p=93e20500)