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Qualitative Constraint Networks (QCNs) are foundational to Qualitative Spatial and Temporal Reasoning (QSTR) for modelling real-world entity relations, facilitating decision-making and planning. However, perturbations or even inconsistencies in ...$\mathsf{}$

In imperfect information games, the evaluation of a game state not only depends on the observable world but also relies on hidden parts of the environment. As accessing the obstructed information trivialises state evaluations, one approach to ...

We consider the problem of univariate time series prediction from an elementary machine learning point of view. Beginning with the question of whether and how Principal Component Analysis (PCA) can be used for time series prediction, we describe a ...

In this paper we investigate alternative data augmentation strategies for Visual Reinforcement Learning and explore the potential benefits of fine-tuning a pretrained image encoder to enhance the learning process. We propose an innovative approach ...

In this paper we refine the concept of anticipation within a card game, taking the Nullspiel in Skat as a running example. We generate the belief space of all distributions of cards according to the assumption on plausible play of the declarer. ...

Inspired by recent advances in Computer Skat and Bridge, this paper investigates automated play for several other trick-taking card games like Belote, Tarot, Doppelkopf, Spades, Hearts, Euchre, and Schafkopf. We present a general framework that ...

Collaborative causal inference (CCI) is a federated learning method for pooling data from multiple, often self-interested, parties, to achieve a common learning goal over causal structures, e.g. estimation and optimization of treatment variables ...

We introduce SaVeWoT (Scripting and Verifying Web of Things Systems), an approach for designing, formally verifying, and deploying decentralized control systems based on the W3C WoT. SaVeWoT consists of two main parts: the SaVeWoT language and the ...

Passive Acoustic Monitoring (PAM) has become a key technology in wildlife monitoring, providing vast amounts of acoustic data. The recording process naturally generates multi-label datasets; however, due to the significant annotation time required,...

Trustworthy AI encompasses various requirements for AI systems, including explainability, fairness, privacy, and robustness. Addressing these dimensions concurrently is challenging due to inherent tensions and trade-offs between them. Current ...

Out-of-distribution detection ensures trustworthiness in machine learning systems by detecting anomalous data points and adjusting confidence in predictions accordingly. However, another key use-case of out-of-distribution detection is the ...

Recent research demonstrates geographic biases in various Large Language Models that reflects common human biases, which are presumably present in the training data. We hypothesize that these biases also exist on smaller scales. Within Germany, ...

Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between objects in a relational domain. At the same time, the field of ...

Recent breakthroughs in n Artificial Intelligence (AI) are poised to transform many domains, including autonomous railway transportation systems. However, safety is essential in this high-stake, safety-critical domain. To ensure compliance with ...

Distributing limited resources among a group of agents is a fundamental challenge in both algorithmic decision support systems and everyday life. The goal of achieving a socially desirable allocation of these resources instead of mere economic ...

In the field of automated reasoning, practical applications often face a significant challenge: knowledge bases are typically too large to be fully processed by theorem provers. To still be able to prove that a given goal follows from a large ...

This research proposes an effective way to address the issues faced by pick-up and delivery services. The real-world variables that affect delivery routes are frequently overlooked by traditional routing technologies, resulting in differences ...

Occupational fraud, the deliberate misuse of company assets by employees, causes damages of around 5% of yearly company revenue. Recent work therefore focuses on automatically detecting occupational fraud through machine learning on the company ...

Data collection and annotation are time-consuming, resource-intensive processes that often require domain expertise. Existing data collections such as animal sound collections provide valuable data sources, but their utilization is often hindered ...

Artificial intelligence has empowered digitalization into a new era of intelligent systems. Machine learning solutions are being tailored to various application scenarios, leading to automated functionalities along complex real-world processes. In ...

The aging population poses numerous challenges to healthcare, including the increase in chronic wounds in the elderly. The current approach to wound assessment by therapists based on photographic documentation is subjective, highlighting the need ...

Many real world applications of artificial intelligence and machine learning require to solve a given task inside a hybrid action space. While it is possible to approach these situations with frameworks based solely on reinforcement learning (RL), ...

Reliable tree log detection is a key requirement for automation of forestry operations. Despite the substantial progress regarding object detection in general, tree log detection lags behind due to the lack of well-annotated datasets. In order to ...${}^{}$

Convolutional Neural Networks (CNNs) are known for their ability to learn hierarchical structures, naturally developing detectors for objects, and semantic concepts within their deeper layers. Activation maps (AMs) reveal these saliency regions, ...

Deep Reinforcement Learning (DRL) has proven its usefulness in various fields, such as robotic control systems, recommendation algorithms, and natural language dialogue interfaces. Recently, we have been witnessing a growing interest in applying ...

In Explanatory Interactive Machine Learning (XIML), counterexamples refine machine learning models by augmenting human feedback. Traditionally created through random sampling or data augmentation, the emergence of Large Language Models (LLMs) now ...