3 research outputs found
The Emerging Landscape of Explainable AI Planning and Decision Making
In this paper, we provide a comprehensive outline of the different threads of
work in Explainable AI Planning (XAIP) that has emerged as a focus area in the
last couple of years and contrast that with earlier efforts in the field in
terms of techniques, target users, and delivery mechanisms. We hope that the
survey will provide guidance to new researchers in automated planning towards
the role of explanations in the effective design of human-in-the-loop systems,
as well as provide the established researcher with some perspective on the
evolution of the exciting world of explainable planning
Explainable Composition of Aggregated Assistants
A new design of an AI assistant that has become increasingly popular is that
of an "aggregated assistant" -- realized as an orchestrated composition of
several individual skills or agents that can each perform atomic tasks. In this
paper, we will talk about the role of planning in the automated composition of
such assistants and explore how concepts in automated planning can help to
establish transparency of the inner workings of the assistant to the end-user
Machine Reasoning Explainability
As a field of AI, Machine Reasoning (MR) uses largely symbolic means to
formalize and emulate abstract reasoning. Studies in early MR have notably
started inquiries into Explainable AI (XAI) -- arguably one of the biggest
concerns today for the AI community. Work on explainable MR as well as on MR
approaches to explainability in other areas of AI has continued ever since. It
is especially potent in modern MR branches, such as argumentation, constraint
and logic programming, planning. We hereby aim to provide a selective overview
of MR explainability techniques and studies in hopes that insights from this
long track of research will complement well the current XAI landscape. This
document reports our work in-progress on MR explainability