One of the original motivations for domain-independent planning was to generate plans that
would then be executed in the environment. However, most existing planners ignore the …

Efficient sampling from constraint manifolds, and thereby generating a diverse set of
solutions for feasibility problems, is a fundamental challenge. We consider the case where a …

Metareasoning suffers from the heterogeneity problem, in which different researchers build
diverse metareasoning models for intelligent systems with comparable functionality but …

AI Planning require computing the costs of ground actions. While often assumed to be
negligible, the run-time of this computation can become a major component in the overall …

Off-road and unstructured environments often contain complex patches of various types of
terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle …

The longest simple path and snake-in-a-box are combinatorial search problems of
considerable research interest. We create a common framework of longest constrained path …

The shortest path problem in graphs is a cornerstone of AI theory and applications. Existing
algorithms generally ignore edge weight computation time. We present a generalized …

The shortest path problem in graphs is fundamental to AI. Nearly all variants of the problem
and relevant algorithms that solve them ignore edge-weight computation time and its …

We address the challenge of finding a shortest path in a graph with unknown obstacles
where the exploration cost to detect whether a state is free or blocked is very high (eg, due to …

Information about action costs is critical for real-world AI planning applications. Rather than
rely solely on declarative action models, recent approaches also use black-box external …