Modeling Synergies in Large Human-Machine Networked Systems
Abstract
Network centric military systems (NCW) involve hundreds to thousands of manned and autonomous entities cooperating to achieve complex joint objectives in incomplete information environments. The overall goal of this multidisciplinary research is to provide validated theories and models, grounded in experiments with human operators that allow descriptive and predictive characterization of important properties and performance of complex and large-scale human-machine networked systems. The most significant results of the research were: (a) a scalable cognitive model framework that provides scalability while maintaining targeted cognitive fidelity (b) algorithms for automated large scale path planning robot systems, (c) predicting behavior, including vulnerabilities, of large scale heterogeneous complex networks, (d) algorithms for constrained multi-robot task assignment (e) scalable models of human robot control for independently operating robots, (f) robot self-reflection and queuing algorithms to schedule operator attention, (g) scalable displays, (h) models of human-robot decision making,, (j) models for planning and resource allocation in multi-robot teams with formal performance guarantees, and (k) human-automation collaborative scheduling.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 25, 2013
- Accession Number
- ADA591084
Entities
People
- Christian Lebiere
- Jonathan How
- Katia Sycara
- Mark Campbell
- Michael Lewis
- Missy Cummings
- Paul Scerri
- Raja Parasuraman
Organizations
- Carnegie Mellon University