Urban intersections represent a complex environment for autonomous vehicles with many
sources of uncertainty. The vehicle must plan in a stochastic environment with potentially …

Adaptive Informative Path Planning with Multi-modal Sensing (AIPPMS) considers the
problem of an agent equipped with multiple sensors, each with different sensing accuracy …

Identifying and eliminating failure scenarios is critical in the development of autonomous
vehicle (AV) systems. However, finding such failures through real-world vehicle-level testing …

Abstract Research in Artificial Intelligence has shown that machines can be programmed to
perform as well as, or even better than humans in specific tasks, such as playing Chess …

Abstract Formula 1 (F1) is one of the most competitive categories of motorsport racing, in
which single-seater, high-performance cars compete around a closed circuit. In F1 events …

Building sophisticated computer players for games has been of interest since the advent of
artificial intelligence research. Monte Carlo tree search (MCTS) techniques have led to …

Autonomous systems are rapidly making their way into the physical domain, where they
have to interact with human users in unstructured and safety-critical scenarios. The …

A growing population will require more metal in order to sustainably build a high quality of
life. However, there have been fewer discoveries of mineral deposits due to constrained …

Conclusion. Our extensions to existing online planning algorithms tackle manyagent
MPOMDPs efficiently, achieving high performance. Future work consists of learning factored …

Within the domain of Reinforcement Learning, the task of online planning in continuous
stochastic environments has proven to be challenging. Over the years, many sparse …