Recent advances in Machine Learning and Deep Learning in particular have lead to the imminent introduction of AI agents into a wide variety of applications. However, the apparent “black-box” nature of these approaches hinders their application in both critical systems and close human-robot interactions. The sub-field of eXplainable Artificial Intelligence (XAI) aims to address this shortcoming. This lecture will introduce and discuss various concepts and methods of XAI and consider them from perspective of Robot Learning and Human-Robot Interaction.

The lecture will start with a (brief) introduction into relevant deep learning approaches, before discussing interpretable scene, task and behavior representations. Afterward the lecture will consider itself with Data-Driven and Goal-Driven AI. Finally, first approaches that incorporate XAI and XAI-based human feedback directly into the learning process itself will be discussed. An exemplary list of topics is given below:

• Introduction to XAI

◦  Interpretable Machine Learning vs Explainable Machine Learning

• Primer / Introduction to relevant Deep Learning Concepts

◦  MLPs and CNNs

◦  Graph Neural Networks

◦  Transformers

◦  Diffusion Models

◦  Score Based Methods

• Interpretable Structures

◦  Scene Representations

◦  Task Representations

◦  Behavior Representations

• Data-Driven Explainable AI: XAI Methods for

◦  Shapley Values

◦  Saliency Maps

◦  Concept Activation Vectors

◦  Linguistic Neuron Annotation

• Goal-Driven Explainable AI: XAI Methods for

◦  Generative Explaining Models

◦  Behavior Verbalization

◦  Behavior Visualization

• Interactive Learning

◦  Integrating Human Feedback

◦  Explanatory Interactive Learning

• Experience in Machine Learning is recommended, e.g. through prior coursework.
  • The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial

Python / PyTorch experience could be beneficial when we discuss practical examples/implementations.

Arbeitsaufwand = 90h = 3 ECTS

• ca 30h Vorlesungsbesuch
• ca 30h Nachbearbeitung
• ca 30h Prüfungsvorbereitung

Als Blockvorlesung gegen Ende des Semesters

KIT-Fakultät für Informatik/1. Informatik Lehrveranstaltungen/1.10 Wahlvorlesungen