Daniel Bresnahan is a faculty member at the University of Nebraska at Omaha within the College of Information Science and Technology, Department of Computer Science. His research focuses on computational logic and algorithm optimization, particularly in the domain of Answer Set Programming (ASP). His work centers on improving the efficiency of logic programming systems through grounding size estimation and program rewriting techniques. Key publications include: System Predictor: Grounding Size Estimator for Logic Programs under Answer Set Semantics (2023) Grounding Size Predictions for Answer Set Programs (2019) Automatic Program Rewriting for Non-Ground Answer Set Programs (2019) These contributions explore performance prediction, combinatorial problem solving, and automated transformation of logic programs, with a focus on enhancing system efficiency through declarative programming methods.
Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.
Johannes Klaus Fichte is an Associate Professor at Linköping University, affiliated with the Department of Computer and Information Science (IDA) and the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His research focuses on computational algorithmics, complexity, and practical applications such as parameterized algorithms for combinatorial problems, model counting of propositional formulas, Boolean satisfiability (SAT), and answer-set programming (ASP). He also has industry experience in healthcare data analysis. His work bridges theoretical foundations and practical implementations, with recent contributions to plan reasoning, bounded treewidth algorithms, and abstract argumentation frameworks. He collaborates with international researchers and publishes in top conferences like AAAI, ECAI, and KR. As part of the AIICS division, he contributes to teaching and research in AI, theoretical computer science, and integrated systems. His research trends emphasize algorithmic efficiency, complexity analysis, and interdisciplinary applications in healthcare and formal methods.
Rolf Schwitter is a Senior Lecturer at the School of Computing, Macquarie University. His research focuses on natural and formal language processing, including controlled natural languages, answer extraction, knowledge representation, probabilistic logic programming, and Semantic Web technologies. He holds an h-index of 13 with over 944 citations. His work emphasizes bridging human-readable and machine-processable systems, particularly in legal and technical domains. Key research contributions include developing frameworks like PENG ASP for declarative programming in natural language, smart contract systems, and hybrid explainability tools like HESIP. He leads projects such as Policy Automation: Reconstructing Policy Documents for Objective Decision Making (2018–present) and has collaborated on initiatives like TwitterNews+ for real-time event detection. Research Themes: Controlled Natural Languages, Smart Contracts, Explainable AI, Legal Informatics Notable Projects: User-guided legal document processing, error-free smart contracts, hybrid prediction explanations Received the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Active in academic publishing with over 117 research outputs spanning conferences, journals, and book chapters.
Joaquín Arias is an Associate Professor at King Juan Carlos University since 2025. His career includes 2020-2025 as Assistant Professor at the same institution and prior pre-doctoral research at IMDEA Software Institute (2013-2020). He has collaborated with institutions like University of Texas at Dallas and Aalto University. Education: Ph.D. in Computer Science (2020, Universidad Politécnica de Madrid) M.Sc. in Computer Science (2015) B.Sc. in Computer Science (2014) M.Arch. in Architecture (2002) Research interests revolve around Constraint Logic Programming , Answer Set Programming , and their applications in Event Calculus , Stream Data Analysis , and Value-Aware Systems . He has developed frameworks like s(CASP) for non-grounded reasoning and Mod TCLP for tabled constraints. Recent articles demonstrate expertise in integrating Large Language Models with logic programming, automated legal reasoning , and real-time system verification . His work emphasizes explainability, constraint handling, and semantic coherence in AI systems. Scientific awards include the best paper prize at CAEPIA 2021 . He has contributed to proceedings as editor for ICLP workshops and authored numerous papers in TPLP , PADL , and AI & LAW .
Prof. Martin Gebser is a University Professor and Deputy Director at the Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt. His work bridges theoretical advancements in Answer Set Programming (ASP) with practical applications in industrial scheduling, semiconductor manufacturing, and explainable AI systems. Institute for Artificial Intelligence and Cybersecurity, University of Klagenfurt His research focuses on Answer Set Programming and its extensions for complex scheduling problems, particularly in semiconductor production. Key areas include: Multi-shot ASP solving for job-shop decomposition Hybrid AI systems integrating reinforcement learning and logic programming Explainable AI for battery health monitoring and semiconductor dispatching Recent publications emphasize temporal planning, constraint learning, and real-world data integration. He has developed customizable simulators and optimization frameworks for industrial applications.
Joaquín Arias is an Associate Professor at the Universidad Rey Juan Carlos (URJC) in Madrid, Spain, since 2025. He previously served as an Assistant Professor at URJC (2020–2025) and as a Pre-Doctoral Researcher at the IMDEA Software Institute (2013–2020). He holds a Ph.D. in Computer Science (2020), a Master of Science in Computer Science (2015), a Bachelor of Science in Computer Science (2014), and a Master of Architecture (2002), all from the Universidad Politécnica de Madrid (UPM). His research focuses on constraint logic programming, answer set programming (ASP), and their applications in stream data analysis, spatial reasoning, and legal reasoning systems. His main research interests include (Constraint) Logic Programming, TCLP, s(CASP), and value-aware systems. He has developed frameworks like Modular TCLP and ATCLP for constraint integration and aggregate computation. Notable projects include applying s(CASP) to legal reasoning (s(LAW)), BIM modeling, and automated conversational agents using LLMs and ASP. His work emphasizes explainable AI and ethical alignment in automated decision-making. Key contributions include the Automated Legal Reasoning with Discretion framework (2024), a socialbot leveraging LLMs and ASP (2024), and a legal reasoning system for administrative processes (2024). He has contributed to proceedings of the ICLP workshops and edited volumes for conferences like AAAI and CAEPIA. His academic awards include the Best Paper Prize at CAEPIA 2021. Collaborations include the University of Texas at Dallas (2017), Aalto University (2019), and IMDEA Software Institute. His teaching includes promoting logic programming in computer science curricula, as highlighted in his 2023 SIIE conference paper.
Zeynep Gözen Saribatur Yaman is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Technische Universität Wien (TU Wien). Her role is supported by the Austrian Science Fund (FWF) as a Projektassistentin (Dr.in techn.). She is affiliated with the DBAI group and contributes to multiple research projects including AURA (2022–2026), DynaCon (2017–2020), AI4EU (2019–2021), and HumanE-AI-Net (2020–2024). Her primary affiliation is with TU Wien’s Faculty of Informatics, where she focuses on advancing explainable AI through abstraction techniques in logic-based systems. Zeynep holds a Doctorate in Technical Sciences (Dr.techn.) from TU Wien (2019), where her dissertation addressed Abstraction for reasoning about agent behavior with answer set programming . She also holds an MSc in a relevant field, though its specifics are not explicitly detailed in the text. Her research spans multiple funded initiatives, emphasizing both theoretical contributions and applied work in robotics and agent systems. Her research interests revolve around abstraction mechanisms in Answer Set Programming (ASP), argumentation frameworks , and their applications to explainable AI , robotics planning , and agent behavior modeling . She explores techniques to reduce complexity in logic-based systems while preserving critical reasoning aspects, with a focus on making AI systems more transparent and understandable. Zeynep has contributed to several projects aiming to enhance AI reasoning through abstraction. Her work bridges formal methods and practical AI challenges, such as reasoning about dynamic environments and multi-agent systems. She actively participates in international conferences and workshops, including KR, AAMAS, ICAPS, and EPIA, where she presents advancements in knowledge representation and reasoning. Her advising record is not explicitly stated in the provided texts. She has collaborated on grants from FWF, EU Horizon 2020, and other competitive funding bodies. Her research also intersects with cognitive factories and hybrid reasoning systems for robotics applications. As part of the DBAI group at TU Wien, she contributes to the development of AI tools and methodologies that prioritize comprehensibility and scalability. Her lab affiliations include the Knowledge-Based Systems Group (DBAI), where she works on theoretical and applied AI challenges.
Dr. Peter Schüller is a Professor affiliated with the Department of Knowledge-Based Systems at TU Wien (Vienna University of Technology). He holds the academic title of Privatdozent (Priv.-Doz.) and has a background in Technical Engineering (Dipl.-Ing. Dr.techn. / Bakk.techn.). His roles include academic research, consulting services for intelligent automation, and partnership with Potassco Solutions. He is based at Favoritenstrasse 11, Room HG0312, and can be contacted via peter.schueller@tuwien.ac.at or contact@peterschueller.com. Education : Master's Thesis: 'Reconstructing borders of manually torn paper sheets using integer linear programming' (2008) PhD (Dr.techn.) in Informatics Habilitation (Privatdozent) qualification Research Interests : Schüller specializes in declarative problem solving through Answer Set Programming (ASP), with focus areas including hybrid knowledge integration systems, inconsistency management, and applications in robotics, traffic optimization, and industrial automation. His work bridges theoretical advances in computational logic with practical implementations in enterprise software architecture and database systems. Projects & Grants : He has led projects funded by the Austrian Research Promotion Agency (FFG), Austrian Science Fund (FWF), and Vienna Science and Technology Fund (WWTF). Key projects include: 'Dynamic knowledge-based (re)configuration of cyber-physical systems' (2017–2020) 'Integrated Evaluation of Answer Set Programs' (2015–2018) 'Inconsistency Management for Knowledge-Integration Systems' (2009–2012) Consulting & Partnerships : Provides services in enterprise software design, database optimization, GDPR compliance, and hybrid knowledge systems. Official partner of Potassco Solutions. Collaborates on initiatives like AI4EU and HumanE-AI-Net. Labs & Teams : Active in TU Wien's Knowledge-Based Systems Group. Involved in developing the DLVHEX and Hexlite solvers.
Leroy Nicholas Chew is a PostDoc Researcher and FWF Projektassistent at the Vienna University of Technology (TU Wien). He is affiliated with the Department of Algorithms and Complexity within the Faculty of Informatics. His roles include contributing to research projects such as QBFPC (2022–2025), Overcoming Intractability in the Knowledge Compilation Map, and REVEAL-AI (2020–2024). These projects reflect his focus on advancing theoretical computer science and automated reasoning methodologies. While specific educational details are not explicitly provided in the text, Leroy Nicholas Chew holds a PhD, as indicated by his role listing. His current position suggests a strong background in computer science and theoretical foundations, consistent with his research activities. His research interests span several key areas in theoretical computer science, including proof complexity, quantified Boolean formulas (QBF), automated reasoning, and knowledge compilation. He explores the hardness of computational problems in logical frameworks, such as analyzing resolution and CDCL proof systems, developing optimal dual proof systems for answer set programming (ASP), and investigating model counting techniques. His work often bridges foundational theory with practical applications in formal verification and algorithm design. Recent publications (2024) highlight advancements in circuits and proofs, model counting, and ASP-QRAT proof systems. Earlier work (2016–2022) addressed QBF resolution calculi, dependency schemes, and certification challenges. These trends underscore his specialization in formal methods and computational logic. No scientific awards are explicitly mentioned in the provided text. In addition to his research, Chew is involved in multiple funded projects. These include the FWF-supported QBFPC (2022–2025), which examines QBF proofs and certificates, and the REVEAL-AI project (2020–2024), focusing on overcoming intractability in knowledge compilation. While specific grant details beyond project funding are not mentioned, his participation underscores his role in collaborative, grant-funded research initiatives. No formal advisees are listed. Chew is part of the Algorithms and Complexity department at TU Wien, collaborating on projects that emphasize proof systems, formal verification, and algorithmic foundations. His work integrates theoretical insights with practical computational methods.
Marcello Balduccini is the Department Chair and Associate Professor of Decision and System Sciences at Saint Joseph's University's Erivan K. Haub School of Business. His research focuses on knowledge representation & reasoning, ontologies, agent architectures, and cybersecurity applications in cyber-physical systems (IoT) and cognitive robotics. He previously held roles as an Assistant Research Professor at Drexel University and Principal Research Scientist at Kodak Research Labs. Research Interests: Knowledge Representation & Reasoning Cyber-Security and Cyber-Analytics Ontology-Based Systems Natural Language Understanding Constraint Satisfaction Problems Trustworthiness in AI/Robotics Recent work emphasizes explainable AI (XAI) systems for Answer Set Programming (ASP), cybersecurity frameworks, and formal methods for cyber-physical systems. His over 100 publications span conferences like LPNMR and ICLP, addressing topics from actual causation to autonomous UAV mission planning. Dr. Balduccini has organized international conferences and received grants supporting AI research, including travel grants for knowledge representation conferences. His work bridges theoretical advancements with practical applications in smart grids, supply chain management, and SDG-aligned AI systems.
Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.
Johannes Oetsch is a researcher at TU Wien's Forschungsbereich Knowledge Based Systems within the Faculty of Informatics. His work focuses on Answer Set Programming (ASP) , neuro-symbolic computing , and visual question answering systems . He holds a Diplom-Ingenieur (Dipl.-Ing.) and a Doctor of Technical Sciences (Dr.techn.) in informatics. Key research areas include: Integration of large language models with symbolic reasoning frameworks Optimization techniques in ASP for scheduling problems Explainability mechanisms for neuro-symbolic systems Recent work emphasizes visual question answering using graph-based representations and contrastive explanation methods. He has contributed to the development of ALASPO , an adaptive optimization framework for ASP solvers. His research also explores applications in manufacturing scheduling and automated testing of logic programs. Notable contributions include: Neuro-symbolic pipelines combining ASP with vision-language models Lexicographical makespan optimization in parallel machine scheduling Large-neighbourhood search strategies for ASP-based optimization
Dr. Sarah Alice Gaggl is a Researcher at the International Center for Computational Logic (ICCL) within the Technische Universität Dresden , where she has served as Group Leader for Logical Programming and Argumentation since October 2020. She also leads the BMBF-funded project NAVAS - Navigation in the Solution Space of Answer Sets and was a Principal Investigator in the Collaborative Research Center 248 (CPEC) from 2019 to 2022. Her research centers on Abstract Argumentation , Answer Set Programming (ASP) , and Knowledge Representation . Doctorate in Computer Science, Vienna University of Technology, March 2013 Her work bridges Answer Set Programming with practical applications in Knowledge Representation and Nonmonotonic Reasoning , focusing on navigation in plan spaces, efficient algorithms for ASP, and multi-criteria answer set selection. She has supervised numerous theses on topics like Multi-Shot ASP , Abstract Argumentation Frameworks , and Reinforcement Learning in game environments. Recent publications highlight trends in Answer Set Navigation (e.g., PlanPilot , IASCAR ), Algorithm Refinement (e.g., Winning Snake ), and Rule-Based Argumentation (e.g., Grounding Rule-Based Argumentation ). Her contributions span Computational Logic , Automated Reasoning , and Software Systems . She has held editorial and organizational roles for journals and conferences, including the Argument & Computation journal, KR , IJCAI , and ICLP . Dr. Gaggl leads the Logical Programming and Argumentation group and has been affiliated with the Computational Logic Group at TU Dresden since 2013. Her teaching includes courses like Theoretical Computer Science & Logic and Advanced Problem Solving .
Ronald de Haan is an Assistant Professor at the Institute for Logic, Language & Computation (ILLC) , University of Amsterdam, with primary affiliation in Theoretical Computer Science (TCS) and secondary affiliation in Mathematical & Computational Logic (MCL) . Since December 2019, he has held this position, following a postdoctoral role at the same institution from 2017 to 2019. He completed his PhD at the Algorithms and Complexity Group at Technische Universität Wien in 2016. Education: PhD in Computer Science, Technische Universität Wien (2016) MSc in Computational Logic, European Master's Program in Computational Logic (2010–2012) BSc in Cognitive Artificial Intelligence & BA in Linguistics, Utrecht University (2007–2010) Research Interests: His work lies at the intersection of theoretical computer science and artificial intelligence , with a strong emphasis on parameterized complexity theory . He explores the computational complexity of problems in AI, knowledge representation & reasoning, and computational logic. Specific areas include the Polynomial Hierarchy, subexponential-time complexity, the Exponential Time Hypothesis, and parameterized compilability. Scientific Awards: E.W. Beth Dissertation Prize 2017 for his PhD thesis "Parameterized Complexity in the Polynomial Hierarchy" Shortlisted for the Heinz Zemanek Prize 2018 Nominated for the GI-Dissertationspreis 2016 by the German Informatics Society Teaching & Supervision: He has taught a wide range of courses at the University of Amsterdam, including Computational Complexity , Knowledge Representation and Reasoning , and Recursion Theory for MSc Logic and MSc AI programs. He also supervises student research projects and theses, offering topics in ASP, complexity theory, and logic programming. Academic Service: He has served on the program committees of top-tier AI and logic conferences such as AAAI, IJCAI, KR, ECAI, and AAMAS, and co-organized events like PhDs in Logic VII.
