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22nd International Conference of the Italian Association for Artificial Intelligence, AIxIA 2023

作者： Kain, Tobias Tompits, Hans Institute of Logic and Computation Knowledge-Based Systems Group E192-03 Technische Universität Wien Vienna1040 Austria

ISBN: (纸本)9783031475450

A significant challenge in the domain of autonomous vehicles is to ensure a reliable and safe operation in a multitude of contexts. As a consequence, autonomous vehicles must be capable of handling various context changes, such as changing weather conditions as well as software and hardware faults, without human support. To address this issue, we introduce a context-based software configuration tool for autonomous vehicles, called ReConf, which is embedded into Aptus, a generic framework for extending system architectures of autonomous vehicles with a self-managing functionality proposed in previous work. ReConf reconfigures the autonomous driving system in case the context changes by means of a reasoning component based on answer-set programming in order to determine system configurations that fulfill the requirements of the current context. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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KIRETT - A wearable device to support rescue operations using artificial intelligence to improve first aid 8

KIRETT - A wearable device to support rescue operations usin...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Zenkert, Johannes Weber, Christian Nadeem, Mubaris Bender, Lisa Fathi, Madjid Ahammed, Abu Shad Ezekiel, Aniebiet Micheal Obermaisser, Roman Bradford, Maximilian Institute of Knowledge Based Systems University of Siegen Siegen Germany University of Siegen Embedded Systems Siegen Germany

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

This short paper presents first steps in the scientific part of the KIRETT project, which aims to improve first aid during rescue operations using a wearable device. The wearable is used for computer-aided situation recognition by means of artificial intelligence. It provides contextual recommendations for actions and operations to rescue personnel and is intended to minimize damage to patients due to incorrect treatment, as well as increase the probability of survival. The paper describes a first overview of research approaches within the project. © 2022 IEEE.

关键词： Neural networks

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A ROS 2-based Navigation and Simulation Stack for the Robotino

A ROS 2-Based Navigation and Simulation Stack for the Rob...

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27th RoboCup International Symposium, 2024

作者： Borse, Saurabh Viehmann, Tarik Ferrein, Alexander Lakemeyer, Gerhard The Mobile Autonomous Systems and Cognitive Robotics Institute FH Aachen University of Applied Science Aachen52066 Germany Knowledge-Based Systems Group RWTH Aachen University Aachen52074 Germany

ISBN: (纸本)9783031858581

The Robotino, developed by Festo Didactic, serves as a versatile platform in education and research for mobile robotics tasks. However, there currently is no ROS 2 integration for the Robotino available. In this paper we describe our work on a Webots simulation environment for a Robotino platform extended by LIDAR sensors. A ROS 2 integration and a pre-configured setup for localization and navigation using existing ROS packages from the Nav2 suite is provided. We validate our setup by comparing simulations with real-world experiments conducted by three Robotinos in a logistics environment in our lab. Additionally, we tested the setup using a ROS 2 hardware driver for the Robotino developed by team GRIPS of the RoboCup Logistics League. The results demonstrate the feasibility of using ROS 2 and Nav2 for navigation tasks on the Robotino platform showing great consistency between simulation and real-world performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mobile robots

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Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

arXiv

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arXiv 2023年

作者： Abu-Rasheed, Hasan Weber, Christian Fathi, Madjid Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view.

关键词： Recommender systems

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LLM-Assisted knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations

arXiv

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arXiv 2025年

作者： Abu-Rasheed, Hasan Jumbo, Constance Amin, Rashed Al Weber, Christian Wiese, Veit Obermaisser, Roman Fathi, Madjid Institute for Knowledge-Based Systems and Knowledge Management University of Siegen Siegen Germany Institute for Embedded Systems University of Siegen Siegen Germany Department of Digital Health Sciences and Biomedicine University of Siegen Siegen Germany

While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms. This paper introduces an innovative approach to higher education curriculum modelling that utilizes large language models (LLMs) for knowledge graph (KG) completion, with the goal of creating personalized learning-path recommendations. Our research focuses on modelling university subjects and linking their topics to corresponding domain models, enabling the integration of learning modules from different faculties and institutions in the student’s learning path. Central to our approach is a collaborative process, where LLMs assist human experts in extracting high-quality, fine-grained topics from lecture materials. We develop a domain, curriculum, and user models for university modules and stakeholders. We implement this model to create the KG from two study modules: Embedded systems and Development of Embedded systems Using FPGA. The resulting KG structures the curriculum and links it to the domain models. We evaluate our approach through qualitative expert feedback and quantitative graph quality metrics. Domain experts validated the relevance and accuracy of the model, while the graph quality metrics measured the structural properties of our KG. Our results show that the LLM-assisted graph completion approach enhances the ability to connect related courses across disciplines to personalize the learning experience. Expert feedback also showed high acceptance of the proposed collaborative approach for concept extraction and classification. © 2025, CC BY.

关键词： knowledge graph

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Using SAT and Partial MaxSAT for Reasoning with System Z and System W 22

Using SAT and Partial MaxSAT for Reasoning with System Z and...

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22nd International Workshop on Nonmonotonic Reasoning, NMR 2024

作者： Beierle, Christoph Spang, Aron Haldimann, Jonas Knowledge Based Systems FernUniversität in Hagen Hagen58084 Germany Institute of Logic and Computation TU Wien Vienna1040 Austria University of Cape Town CAIR Cape Town South Africa

Nonmonotonic reasoning from conditional belief bases typically depends on a structure over possible worlds that relies on the verification and falsification of conditionals. A major challenge in implementing such reasoning approaches is that the number of worlds in these structures grows exponentially with the number of propositional variables occurring in the belief base. For addressing this problem by using the power of current solvers, recently an implementation of reasoning with system W using Partial MaxSAT problems has been proposed. In this paper, we investigate this approach in more detail, present a formal correctness proof of the system W inference algorithm SWinf, and extend its empirical evaluation. Furthermore, we show that the approach can be transferred to implementing Pearl’s system Z by using SAT problems, and prove the correctness of the resulting system Z inference algorithm SZinf. Our implementations of system Z and system W demonstrate that they outperform previous implementations and allow for signature and knowledge base sizes that have been infeasible before. © 2024 Copyright for this paper by its authors.

关键词： Inference engines

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Digit-DM: A Sustainable Data Mining Modell for Continuous Digitization in Manufacturing

Digit-DM: A Sustainable Data Mining Modell for Continuous Di...

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IEEE International Conference on Electro-Information Technology

作者： Christian Weber Peter Czerner Madjid Fathi Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany Elmos Semiconductor SE Dortmund Germany

Manufacturing as an industry is under continuous pressure to deliver the right product, at the right quality, quantity and in time. To do so it becomes increasingly important to detect the source of manufacturing problems in a short amount of time but also to prevent further occurrence of know problems. Data Mining is focused on identifying problem patterns and inferring the right interpretation to trace and resolve the root cause in time. However, lessons learned are rarely transported into digital solutions that then thoroughly enable to automatize detection and resolving of incidents. Data mining models exist, but no structured approach for transforming and sustaining found solutions digitally. We are introducing Digit-DM as a structured and strategic process for digitizing analytical results. Digit-DM is building on top of existing data mining models but defines a strategic process for continuous digitization, enabling sustainable, digital manufacturing support, utilizing analytical lessons learned.

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Feature Ranking for the Prediction of Energy Consumption on CNC Machining Processes

Feature Ranking for the Prediction of Energy Consumption on ...

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2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2024

作者： Kader, Hafez Strobel, Robin Puchta, Alexander Fleischer, Jurgen Noack, Benjamin Spiliopoulou, Myra Institute for Intelligent Cooperating Systems Otto von Guericke University Autonomous Multisensor Systems Group Magdeburg Germany Germany Otto von Guericke University Knowledge Management and Discovery Lab Faculty of Computer Science Magdeburg Germany

ISBN: (纸本)9798350368031

Energy consumption is a critical factor that nega-tively impacts the environment. Sustainable production is essen-tial for addressing the climate crisis, as low-emission manufacturing can both reduce costs and minimize environmental impact. Energy-efficient CNC machine tools significantly contribute to achieving ambitious environmental objectives. In recent years, numerous studies have focused on low-energy consumption production, analyzing factors that contribute to sustainable manufacturing. When using the analytical or empirical model, factors and corrections might be omitted. With advancements in machine learning and the increasing availability of large datasets, models are being developed to predict energy consumption with high accuracy. However, these models often overlook the importance of features that contribute to a transparent prediction process and their influence on the results. In our paper, a LSTM model is initially utilized to predict the energy consumption of CNC machines. Following this, a method is devised to rank the features based on their predictive power, considering temporal variations. We show that some of the features ranked in the top positions agree with independent literature findings, while others are new and demand further investigation. © 2024 IEEE.

关键词： Green manufacturing

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Scaling Up Reasoning from Conditional Belief Bases 16th

Scaling Up Reasoning from Conditional Belief Bases

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16th International Conference on Scalable Uncertainty management, SUM 2024

作者： Beierle, Christoph Haldimann, Jonas Sanin, Arthur Schwarzer, Leon Spang, Aron Spiegel, Lars-Phillip von Berg, Martin Knowledge-Based Systems Faculty of Mathematics and Computer Science FernUniversität in Hagen Hagen58084 Germany Institute of Logic and Computation TU Wien Vienna1040 Austria University of Cape Town and CAIR Cape Town7700 South Africa

ISBN: (纸本)9783031762345

This paper introduces the online reasoning platform InfOCF-Web 2.0 that provides easy access to implementations of various inference methods for conditional belief bases. We present an overview of the realization of the inductive inference operator s p-entailment, system Z, c-inference, and system W. In order to address the fact that the possible worlds to be taken into account grow exponentially with the propositional signature over which the conditionals in the belief base are defined, the implementations employ SAT and Partial MaxSAT concepts and use the power of current SAT and SMT solvers. Our evaluation shows that each of the four inference operators can handle belief bases over signatures containing more than 100 variables and with more than 100 conditionals. Thus, InfOCF-Web 2.0 scales up nonmonotonic reasoning from conditionals to a new dimension because apart from the implementations now available in InfOCF-Web 2.0, there is no other implementation of an inference operator for conditional belief bases for which such problem sizes are feasible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Explainable Answer-set Programming

arXiv

引用

arXiv 2023年

作者： Geibinger, Tobias Knowledge-based Systems Group Institute for Logic and Computation TU Wien Austria

The interest in explainability in artificial intelligence (AI) is growing vastly due to the near ubiquitous state of AI in our lives and the increasing complexity of AI systems. Answer-set Programming (ASP) is used in many areas, among them are industrial optimisation, knowledge management or life sciences, and thus of great interest in the context of explainability. To ensure the successful application of ASP as a problem-solving paradigm in the future, it is thus crucial to investigate explanations for ASP solutions. Such an explanation generally tries to give an answer to the question of why something is, respectively is not, part of the decision produced or solution to the formulated problem. Although several explanation approaches for ASP exist, almost all of them lack support for certain language features that are used in practice. Most notably, this encompasses the various ASP extensions that have been developed in the recent years to enable reasoning over theories, external computations, or neural networks. This project aims to fill some of these gaps and contribute to the state of the art in explainable ASP. We tackle this by extending the language support of existing approaches but also by the development of novel explanation formalisms, like contrastive explanations. © 2023, CC BY.

关键词： knowledge management

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