Supply chain operations traditionally involve a variety of complex decision making problems.
Over the last few decades, supply chains greatly benefited from advances in computation …

The concept of a Digital Twin (DT) has stood out among the emerging digitization
technologies and been embraced by US and EU governments and companies. Practitioners …

Abstract With Artificial Intelligence becoming part of a rapidly increasing number of industrial
applications, more and more requirements about their transparency and trustworthiness are …

In human-aware planning systems, a planning agent might need to explain its plan to a
human user when that plan appears to be non-feasible or sub-optimal. A popular approach …

As AI systems become ever more intertwined in our personal lives, the way in which they
explain themselves to and interact with humans is an increasingly critical research area. The …

Abstract Argumentation frameworks (AFs) provide a central approach to perform reasoning
in many formalisms within argumentation in Artificial Intelligence (AI). Semantics for AFs …

Explanation is necessary for humans to understand and accept decisions made by an AI
system when the system's goal is known. It is even more important when the AI system …

Model Multiplicity (MM), also known as predictive multiplicity or the Rashomon Effect, refers
to a scenario where multiple, equally performing machine learning (ML) models may be …

BDI agents act in response to external inputs and their internal plan library. Understanding
the root cause of BDI agent action is often difficult, and in this paper we present a dialogue …

With dramatic improvements in optimization software, the solution of large-scale problems
that seemed intractable decades ago are now a routine task. This puts even more real-world …