As the performance and complexity of machine learning models have grown significantly
over the last years, there has been an increasing need to develop methodologies to …

This paper presents JEDAI, an AI system designed for outreach and educational efforts
aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated …

Abstract Model Reconciliation Problems (MRPs) and their variant, Logic-based MRPs (L-
MRPs), have emerged as popular methods for explainable planning problems. Both MRP …

Abstract Agent Scheduling Problems (ASPs) are common in various real-world situations,
requiring explainable decision-making processes to effectively allocate resources to multiple …

We introduce DR-HAI--a novel argumentation-based framework designed to extend model
reconciliation approaches, commonly used in explainable AI planning, for enhanced human …

As we grow more reliant on AI systems for an increasing variety of applications in our lives,
the need to understand and interpret such systems also becomes more pronounced, be it for …

Abstract Recent breakthroughs in Artificial Intelligence (AI) have brought the dream of
developing and deploying complex AI systems that can potentially transform everyday life …

" Human-aware" has become a popular keyword used to describe a particular class of AI
systems that are designed to work and interact with humans. While there exists a surprising …

Explanation generation frameworks aim to make AI systems' decisions transparent and
understandable to human users. However, generating explanations in uncertain …

Understanding how robots plan and execute tasks is crucial in today's world, where they are
becoming more prevalent in our daily lives. However, teaching non-experts the complexities …