Deep space network resource scheduling approach and application

Deep Space Network (DSN) resource scheduling is the process of distributing ground-based facilities to track multiple spacecraft. The Jet Propulsion Laboratory has carried out extensive research to find ways of automating this process in an effort to reduce time and manpower costs. This paper presents a resource-scheduling system entitled PLAN-IT with a description of its design philosophy. The PLAN-IT's current on-line usage and limitations in scheduling the resources of the DSN are discussed, along with potential enhancements for DSN application

B-52 stability augmentation system reliability

The B-52 SAS (Stability Augmentation System) was developed and retrofitted to nearly 300 aircraft. It actively controls B-52 structural bending, provides improved yaw and pitch damping through sensors and electronic control channels, and puts complete reliance on hydraulic control power for rudder and elevators. The system has experienced over 300,000 flight hours and has exhibited service reliability comparable to the results of the reliability test program. Development experience points out numerous lessons with potential application in the mechanization and development of advanced technology control systems of high reliability

Count-Based Exploration with the Successor Representation

In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. Our approach is based on the successor representation (SR), which was originally introduced as a representation defining state generalization by the similarity of successor states. Here we show that the norm of the SR, while it is being learned, can be used as a reward bonus to incentivize exploration. In order to better understand this transient behavior of the norm of the SR we introduce the substochastic successor representation (SSR) and we show that it implicitly counts the number of times each state (or feature) has been observed. We use this result to introduce an algorithm that performs as well as some theoretically sample-efficient approaches. Finally, we extend these ideas to a deep RL algorithm and show that it achieves state-of-the-art performance in Atari 2600 games when in a low sample-complexity regime.Comment: This paper appears in the Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020

A strategic plan for MIS at James River Corporation of Virginia

The objectives of this paper are (1) to present a methodology for developing an M.I.S. strategic plan, and (2) to recommend a M.I.S. organizational structure for James River Corporation of Virginia. The paper begins by describing the five businesses of James River and then describes a process for developing a strategic plan for a M.I.S. function. The next two sections define a desired future state for M.I.S. in James River and present guidelines for implementing this future state. The fifth section defines a method of implementing the strategy across the five diverse businesses of the corporation