Brian C. Williams

TL;DR: This work exploits the underlying relations between interacting agents and decouple the joint prediction problem into marginal prediction problems, and first classifies interacting agents as pairs of influencer and reactors, and then leverages a marginal prediction model and a conditional prediction model to predict trajectories for the influencers and reactors.

TL;DR: Chaski is introduced, a multi-agent executive for scheduling temporal plans with online task assignment that enables an agent to dynamically update its plan in response to disturbances in task assignment and the schedule of other agents.

TL;DR: This work first extends the generative adversarial network framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning, and sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes.

TL;DR: The New Millennium Remote Agent (NMRA) will be the first AI system to control an actual spacecraft and to achieve this level of execution robustness, a procedural executive based on generic procedures with a deductive model-based executive is integrated.

TL;DR: This paper extends a previous approach for linear systems that approximates the distribution of the predicted system state using a finite number of particles to give a new method for nonlinear, robust control of nonlinear systems under probabilistic uncertainty.