Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …

Minimum vertex cover (MinVC) is a prominent NP-hard problem in artificial intelligence, with
considerable importance in applications. Local search solvers define the state of the art in …

Anytime planning algorithms often have hyperparameters that can be tuned at runtime to
optimize their performance. While work on metareasoning has focused on when to interrupt …

Due to the complexity of the real world, autonomous systems use decision-making models
that rely on simplifying assumptions to make them computationally tractable and feasible to …

Anytime motion planners are widely used in robotics. However, the relationship between
their solution quality and computation time is not well understood, and thus, determining …

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

Although experts carefully specify the high-level decision-making models in autonomous
systems, it is infeasible to guarantee safety across every scenario during operation. We …

Anytime algorithms offer a trade-off between solution quality and computation time that has
proven to be useful in autonomous systems for a wide range of real-time planning problems …

We introduce the fundamental ideas and challenges of Predictable AI, a nascent research
area that explores the ways in which we can anticipate key validity indicators (eg …

Real-world multi-agent systems such as warehouse robots operate under significant time
constraints–in such settings, rather than spending significant amounts of time solving for …