An introduction to decision making under uncertainty from a computational perspective,
covering both theory and applications ranging from speech recognition to airborne collision …

This work considers the problem of learning cooperative policies in complex, partially observable
domains without explicit communication. We extend three classes of single-agent deep …

One approach to designing decision-making logic for an aircraft collision avoidance system
frames the problem as a Markov decision process and optimizes the system using dynamic …

Tactical decision making for autonomous driving is challenging due to the diversity of
environments, the uncertainty in the sensor information, and the complex interaction with other …

Deep neural networks are revolutionizing the way complex systems are designed. Consequently,
there is a pressing need for tools and techniques for network analysis and certification. …

Deep neural networks have emerged as a widely used and effective means for tackling
complex, real-world problems. However, a major obstacle in applying them to safety-critical …

Abstract Machine learning with missing data has been approached in many different ways,
including feature imputation where missing feature values are estimated based on observed …

A comprehensive introduction to optimization with a focus on practical algorithms for the
design of engineering systems. This book offers a comprehensive introduction to optimization …

Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance
systems must be developed. Instead of hand-crafting a collision avoidance algorithm for …

Deep neural networks are widely used for nonlinear function approximation, with applications
ranging from computer vision to control. Although these networks involve the composition …