Combining machine learning and semantic web: A systematic mapping study

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the Semantic Web community - Semantic Web Machine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology.</p

Explainable and Interpretable Decision-Making for Robotic Tasks

Future generations of robots, such as service robots that support humans with household tasks, will be a pervasive part of our daily lives. The human\u27s ability to understand the decision-making process of robots is thereby considered to be crucial for establishing trust-based and efficient interactions between humans and robots. In this thesis, we present several interpretable and explainable decision-making methods that aim to improve the human\u27s understanding of a robot\u27s actions, with a particular focus on the explanation of why robot failures were committed.In this thesis, we consider different types of failures, such as task recognition errors and task execution failures. Our first goal is an interpretable approach to learning from human demonstrations (LfD), which is essential for robots to learn new tasks without the time-consuming trial-and-error learning process. Our proposed method deals with the challenge of transferring human demonstrations to robots by an automated generation of symbolic planning operators based on interpretable decision trees. Our second goal is the prediction, explanation, and prevention of robot task execution failures based on causal models of the environment. Our contribution towards the second goal is a causal-based method that finds contrastive explanations for robot execution failures, which enables robots to predict, explain and prevent even timely shifted action failures (e.g., the current action was successful but will negatively affect the success of future actions). Since learning causal models is data-intensive, our final goal is to improve the data efficiency by utilizing prior experience. This investigation aims to help robots learn causal models faster, enabling them to provide failure explanations at the cost of fewer action execution experiments.In the future, we will work on scaling up the presented methods to generalize to more complex, human-centered applications

Funciones ejecutables de las representaciones en el aprendizaje de los conceptos algebraicos

This study aimed to examine the role of multiple representations in learning algebraic concepts for high school students. Using the semiexperimental research method for teaching of numerical, symbolic, and graphical representations, and traditional teaching, 83 female students were selected from the tenth grade of a high school in Tehran. We concluded that there is a significant difference between the mean scores of mathematics in the control and experimental groups. Using the method based on different representations helped the students to become creative and provide similar Algebra examples; thereby analysis power will be increased.Este estudio tiene como objetivo examinar el papel de las representaciones múltiples en el aprendizaje de los conceptos algebraicos en estudiantes de educación secundaria. Se desarrolló una investigación semiexperimental para la enseñanza de representaciones numéricas, simbólicas y gráficas y la enseñanza tradicional, en este estudio participaron 83 estudiantes femeninas del décimo grado de una escuela secundaria en Teherán. Se concluyó que hay una diferencia significativa entre los puntajes promedio de matemáticas en el grupo control y los grupos experimentales. El uso del método basado en diferentes representaciones ayudó a las estudiantes a ser creativas y proporcionar ejemplos de álgebra similares; por lo tanto, la capacidad de análisis aumentará