Stepwise Goal-Driven Networks for Trajectory Prediction

Using Recurrent Neural Networks in Trajectory Prediction

Decentralized Goal Assignment and Trajectory Generation in Multi-Robot Networks

This paper considers the problem of decentralized goal assignment and trajectory generation for multi-robot networks when only local communication is available, and proposes an approach based on methods related to switched systems and set invariance. A family of Lyapunov-like functions is employed to encode the (local) decision making among candidate goal assignments, under which the agents pic...

Hierarchical Goal Networks and Goal-Driven Autonomy: Going where AI Planning Meets Goal Reasoning

Planning systems are typically told what goals to pursue and cannot modify them. Some methods (e.g., for contingency planning, dynamic replanning) can respond to execution failures, but usually ignore opportunities and do not reason about the goals themselves. Goal-Driven Autonomy (GDA) relaxes some common assumptions of classical planning (e.g., static environments, fixed goals, no unpredictab...

An Architecture for Goal - Driven

In complex and changing environments explanation must be a a dynamic and goal-driven process. This paper discusses an evolving system implementing a novel model of explanation generation | Goal-Driven Interactive Explanation | that models explanation as a goal-driven, multi-strategy, situated process inter-weaving reasoning with action. We describe a preliminary implementation of this model in ...

Head-Driven Simulation of Water Supply Networks

Up to now most of the existing water supply network analyses have been based on demand-driven simulation models. These models assume that nodal outflows are fixed and are always available. However, this method of simulation neglects the pressure-dependent nature of demand that is characterized by changes in actual nodal outflows particularly during critical events like major mechanical or hydra...