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 …

Smart home systems with AI planning functionality have the potential to improve the lives of
users. However, there is an emerging expectation that users should better understand and …

One of the core challenges of explaining decisions made by modern AI systems is the need
to address the potential gap in the inferential capabilities of the system generating the …

Since its introduction, partial satisfaction planning (PSP), including both oversubscription
(OSP) and net-benefit, has received significant attention in the classical planning …

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

Stochastic shortest path (SSP) problems are a common framework for planning under
uncertainty. However, the reactive structure of their solution policies is typically not easily …

Temporal planning is a hard problem that requires good heuristic and memoization
strategies to solve efficiently. Merge-and-shrink abstractions have been shown to serve as …

Deep learning is increasingly used to learn policies for planning problems. However,
policies represented by neural networks are difficult to interpret, verify and trust. Existing …

The objective of optimal oversubscription planning is to find a plan that yields an end state
with a maximum utility while keeping plan cost under a certain bound. In practice, the …

In oversubscription planning (OSP), not all goals can be achieved. If a global optimization
objective is difficult to fix, then an iterative planning process in which users refine their …