NASA Scientific and Technical Aerospace Reports

flows currently received from states and Regions under the Public Water System Supervision (PWSS) Program. OGWDW will
implement the first stage, addressing the public water system (PWS) facility and compliance data, by the end of 2004. This
Information Strategic Plan document addresses modernizing the two existing Safe Drinking Water Information Systems
supporting the data flow for the PWSS Program, SDWIS-FEDERAL and SDWIS-STATE.
NTIS
Information Systems; Information Management; Data Management; Potable Water
20040070708 NASA Ames Research Center, Moffett Field, CA, USA
Team Formation in Partially Observable Multi-Agent Systems
Agogino, Adrian K.; Tumer, Kagan; [2004]; 6 pp.; In English; International Joint Conference on Neural Networks, July 2004,
Budapest, Hungary; No Copyright; Avail: CASI; A02, Hardcopy
Sets of multi-agent teams often need to maximize a global utility rating the performance of the entire system where a team
cannot fully observe other teams agents. Such limited observability hinders team-members trying to pursue their team utilities
to take actions that also help maximize the global utility. In this article, we show how team utilities can be used in partially
observable systems. Furthermore, we show how team sizes can be manipulated to provide the best compromise between
having easy to learn team utilities and having them aligned with the global utility, The results show that optimally sized teams
in a partially observable environments outperform one team in a fully observable environment, by up to 30%.
Author
Artificial Intelligence; Functions (Mathematics); Dynamical Systems; Algorithms
20040070709 NASA Ames Research Center, Moffett Field, CA, USA
Reliability of Complex Nonlinear Numerical Simulations
Yee, H. C.; January 13, 2004; 1 pp.; In English; No Copyright; Avail: Other Sources; Abstract Only
This work describes some of the procedure to ensure a higher level of confidence in the predictability and reliability (PAR)
of numerical simulation of multiscale complex nonlinear problems. The focus is on relating PAR of numerical simulations
with complex nonlinear phenomena of numerics. To isolate sources of numerical uncertainties, the possible discrepancy
between the chosen partial differential equation (PDE) model and the real physics and/or experimental data is set aside. The
discussion is restricted to how well numerical schemes can mimic the solution behavior of the underlying PDE model for finite
time steps and grid spacings. The situation is complicated by the fact that the available theory for the understanding of
nonlinear behavior of numerics is not at a stage to fully analyze the nonlinear Euler and Navier-Stokes equations. The
discussion is based on the knowledge gained for nonlinear model problems with known analytical solutions to identify and
explain the possible sources and remedies of numerical uncertainties in practical computations. Examples relevant to turbulent
flow computations are included.
Author
Mathematical Models; Nonlinear Systems; Reliability Analysis; Computational Fluid Dynamics; Direct Numerical
Simulation; Complex Systems
20040070711 NASA Ames Research Center, Moffett Field, CA, USA
Filtering in Hybrid Dynamic Bayesian Networks
Andersen, Morten Nonboe; Andersen, Rasmus Orum; Wheeler, Kevin; Journal of Machine Learning Research; October 2000;
Volume 1, pp. 1-48; In English; Copyright; Avail: CASI; A03, Hardcopy
We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation
simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore,
we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a
watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both
experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations
of these. In the watertank simulation, we also demonstrate the use of ‘non-strict’ Rao-Blackwellisation. We show that the
unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended
Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and
sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework
when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the
watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement
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