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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1967

Adaptive age replacement.

Loss structure models concerning age replacement policies, discussing mathematical aspects of maintenance with dynamic programming formulation

COST ESTIMATE↗
NASA NTRS1971

A control-theory model for human decision-making

A model for human decision making is an adaptation of an optimal control model for pilot/vehicle systems. The models for decision and control both contain concepts of time delay, observation noise, optimal prediction, and optimal estimation. The decision making model was intended for situations in which the human bases his decision on his estimate of the state of a linear plant. Experiments are described for the following task situations: (a) single decision tasks, (b) two-decision tasks, and (c) simultaneous manual control and decision making. Using fixed values for model parameters, single-task and two-task decision performance can be predicted to within an accuracy of 10 percent. Agreement is less good for the simultaneous decision and control situation.

Levison, W. H.↗
NASA NTRS1991

Measuring uncertainty by extracting fuzzy rules using rough sets

Despite the advancements in the computer industry in the past 30 years, there is still one major deficiency. Computers are not designed to handle terms where uncertainty is present. To deal with uncertainty, techniques other than classical logic must be developed. The methods are examined of statistical analysis, the Dempster-Shafer theory, rough set theory, and fuzzy set theory to solve this problem. The fundamentals of these theories are combined to possibly provide the optimal solution. By incorporating principles from these theories, a decision making process may be simulated by extracting two sets of fuzzy rules: certain rules and possible rules. From these rules a corresponding measure of how much these rules is believed is constructed. From this, the idea of how much a fuzzy diagnosis is definable in terms of a set of fuzzy attributes is studied.

Worm, Jeffrey A.↗
NASA NTRS1993

Certain and possible rules for decision making using rough set theory extended to fuzzy sets

Uncertainty may be caused by the ambiguity in the terms used to describe a specific situation. It may also be caused by skepticism of rules used to describe a course of action or by missing and/or erroneous data. To deal with uncertainty, techniques other than classical logic need to be developed. Although, statistics may be the best tool available for handling likelihood, it is not always adequate for dealing with knowledge acquisition under uncertainty. Inadequacies caused by estimating probabilities in statistical processes can be alleviated through use of the Dempster-Shafer theory of evidence. Fuzzy set theory is another tool used to deal with uncertainty where ambiguous terms are present. Other methods include rough sets, the theory of endorsements and nonmonotonic logic. J. Grzymala-Busse has defined the concept of lower and upper approximation of a (crisp) set and has used that concept to extract rules from a set of examples. We will define the fuzzy analogs of lower and upper approximations and use these to obtain certain and possible rules from a set of examples where the data is fuzzy. Central to these concepts will be the idea of the degree to which a fuzzy set A is contained in another fuzzy set B, and the degree of intersection of a set A with set B. These concepts will also give meaning to the statement; A implies B. The two meanings will be: (1) if x is certainly in A then it is certainly in B, and (2) if x is possibly in A then it is possibly in B. Next, classification will be looked at and it will be shown that if a classification will be looked at and it will be shown that if a classification is well externally definable then it is well internally definable, and if it is poorly externally definable then it is poorly internally definable, thus generalizing a result of Grzymala-Busse. Finally, some ideas of how to define consensus and group options to form clusters of rules will be given.

Dekorvin, Andre↗
NASA NTRS2002

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Non-NASA Center↗
NASA NTRS2001

Quantifying the performance limits of human saccadic targeting during visual search

In previous studies of saccadic targeting, the issue how visually guided saccades to unambiguous targets are programmed and executed has been examined. These studies have found different degrees of guidance for saccades depending on the task and task difficulty. In this study, we use ideal-observer analysis to estimate the visual information used for the first saccade during a search for a target disk in noise. We quantitatively compare the performance of the first saccadic decision to that of the ideal observer (ie absolute efficiency of the first saccade) and to that of the associated final perceptual decision at the end of the search (ie relative efficiency of the first saccade). Our results show, first, that at all levels of salience tested, the first saccade is based on visual information from the stimulus display, and its highest absolute efficiency is approximately 20%. Second, the efficiency of the first saccade is lower than that of the final perceptual decision after active search (with eye movements) and has a minimum relative efficiency of 19% at the lowest level of saliency investigated. Third, we found that requiring observers to maintain central fixation (no saccades allowed) decreased the absolute efficiency of their perceptual decision by up to a factor of two, but that the magnitude of this effect depended on target salience. Our results demonstrate that ideal-observer analysis can be extended to measure the visual information mediating saccadic target-selection decisions during visual search, which enables direct comparison of saccadic and perceptual efficiencies.

Non-NASA Center↗
NASA NTRS2005

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

AVHRR (ADVANCED VERY HIGH RESOLUTION RADIOMETER)↗
NASA NTRS2018

Item Retrieval as Utility Estimation

Retrieval systems have greatly improved over the last half century, estimating relevance to a latent user need in a wide variety of areas. One area that has not enjoyed such advancements is searching for items by attribute values, a common activity in e-commerce and science, particularly given numeric values. Existing item retrieval systems assume the user has a firm grasp of their own desires and can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database. A contrasting approach would be to estimate how well items match the user?s latent desires and return items ranked by this estimation. Towards this end, we present a retrieval model inspired by multi-criteria decision making theory, concentrating on numeric attributes. We evaluate our novel approach, the de-facto standard of Boolean retrieval, and several models proposed in the literature, in two user studies using Amazon Mechanical Turk. We use a competitive game to motivate test subjects and compare methods based on the results of the subjects? initial query and their success in the game. In our experiments, our new method signi cantly outperformed the others, whereas the Boolean approaches had the worst performance.

operations research↗
NASA NTRS1978

Combined monitoring, decision and control model for the human operator in a command and control desk

A report is given on the ongoing efforts to mode the human operator in the context of the task during the enroute/return phases in the ground based control of multiple flights of remotely piloted vehicles (RPV). The approach employed here uses models that have their analytical bases in control theory and in statistical estimation and decision theory. In particular, it draws heavily on the modes and the concepts of the optimal control model (OCM) of the human operator. The OCM is being extended into a combined monitoring, decision, and control model (DEMON) of the human operator by infusing decision theoretic notions that make it suitable for application to problems in which human control actions are infrequent and in which monitoring and decision-making are the operator's main activities. Some results obtained with a specialized version of DEMON for the RPV control problem are included.

Muralidharan, R.↗
NASA NTRS1998

Decision Processes in Discrimination: Fundamental Misrepresentations of Signal Detection Theory

In the first part of this article, I describe a new approach to studying decision making in discrimination tasks that does not depend on the technical assumptions of signal detection theory (e.g., normality of the encoding distributions). Applying these new distribution-free tests to data from three experiments, I show that base rate and payoff manipulations had substantial effects on the participants' encoding distributions but no effect on their decision rules, which were uniformly unbiased in equal and unequal base rate conditions and in symmetric and asymmetric payoff conditions. In the second part of the article, I show that this seemingly paradoxical result is readily explained by the sequential sampling models of discrimination. I then propose a new, "model-free" test for response bias that seems to more properly identify both the nature and direction of the biases induced by the classical bias manipulations.

Balakrishnan, J. D.↗