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Results for “causal structure”

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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NASA NTRS1990

The structure of random discrete spacetime

The usual picture of spacetime consists of a continuous manifold, together with a metric of Lorentzian signature which imposes a causal structure on the spacetime. A model, first suggested by Bombelli et al., is considered in which spacetime consists of a discrete set of points taken at random from a manifold, with only the causal structure on this set remaining. This structure constitutes a partially ordered set (or poset). Working from the poset alone, it is shown how to construct a metric on the space which closely approximates the metric on the original spacetime manifold, how to define the effective dimension of the spacetime, and how such quantities may depend on the scale of measurement. Possible desirable features of the model are discussed.

Brightwell, Graham↗
NASA NTRS1991

Structure of random discrete spacetime

The usual picture of spacetime consists of a continuous manifold, together with a metric of Lorentzian signature which imposes a causal structure on the spacetime. A model, first suggested by Bombelli et al., is considered in which spacetime consists of a discrete set of points taken at random from a manifold, with only the causal structure on this set remaining. This structure constitutes a partially ordered set (or poset). Working from the poset alone, it is shown how to construct a metric on the space which closely approximates the metric on the original spacetime manifold, how to define the effective dimension of the spacetime, and how such quantities may depend on the scale of measurement. Possible desirable features of the model are discussed.

Brightwell, Graham↗
NASA NTRS1987

Contingent plan structures for spacecraft

Most current AI planners build partially ordered plan structures which delay decisions on action ordering. Such structures cannot easily represent contingent actions. A representation which can is presented. The representation has some other useful features: it provides a good account of the causal structure of a plan, can be used to describe disjunctive actions, and it offers a planner the opportunity of even less commitment than the classical partial order on actions. The use of this representation is demonstrated in an on-board spacecraft activity sequencing problem. Contingent plan execution in a spacecraft context highlights the requirements for a fully disjunctive representation, since communication delays often prohibit extensive ground-based accounting for remotely sensed information and replanning on execution failure.

Drummond, M.↗
NASA NTRS1990

Vision science and technology for supervised intelligent space robots

The focus of recent work in robotic vision for application in intelligent space robots such as the Extravehicular Activity (EVA) Retriever is in visual function, that is, how information about the space world is derived and then conveyed to cognition. The goal of this work in visual function is first to understand how the relevant structure of the surrounding world is evidenced by regularities among the pixels of images, then to understand how these regularities are mapped on the premises that form the primitive elements of cognition, and then to apply these understandings with the elements of visual processing (algorithms) and visual mechanism (machine organization) to intelligent space robot simulations and test beds. Since visual perception is the process of recognizing regularities in images that are known on the basis of a model of the world to be reliable related to causal structure in the environment (because perception attaches meaning to the link between a conception of the environment and the objective environment), the work involves understanding generic, generally applicable models of world structure (not merely objects) and how that structure evidences itself in images.

Erickson, Jon D.↗
NASA NTRS2005

Development and Testing of Data Mining Algorithms for Earth Observation

The new algorithms developed under this project included a principled procedure for classification of objects, events or circumstances according to a target variable when a very large number of potential predictor variables is available but the number of cases that can be used for training a classifier is relatively small. These "high dimensional" problems require finding a minimal set of variables -called the Markov Blanket-- sufficient for predicting the value of the target variable. An algorithm, the Markov Blanket Fan Search, was developed, implemented and tested on both simulated and real data in conjunction with a graphical model classifier, which was also implemented. Another algorithm developed and implemented in TETRAD IV for time series elaborated on work by C. Granger and N. Swanson, which in turn exploited some of our earlier work. The algorithms in question learn a linear time series model from data. Given such a time series, the simultaneous residual covariances, after factoring out time dependencies, may provide information about causal processes that occur more rapidly than the time series representation allow, so called simultaneous or contemporaneous causal processes. Working with A. Monetta, a graduate student from Italy, we produced the correct statistics for estimating the contemporaneous causal structure from time series data using the TETRAD IV suite of algorithms. Two economists, David Bessler and Kevin Hoover, have independently published applications using TETRAD style algorithms to the same purpose. These implementations and algorithmic developments were separately used in two kinds of studies of climate data: Short time series of geographically proximate climate variables predicting agricultural effects in California, and longer duration climate measurements of temperature teleconnections.

Glymour, Clark↗
NASA NTRS2006

Excising das All: Evolving Maxwell waves beyond Scri

We study the numerical propagation of waves through future null infinity in a conformally compactified spacetime. We introduce an artificial cosmological constant, which allows us some control over the causal structure near null infinity. We exploit this freedom to ensure that all light cones are tilted outward in a region near null infinity, which allows us to impose excision-style boundary conditions in our finite difference code. In this preliminary study we consider electromagnetic waves propagating in a static, conformally compactified spacetime.

vanMeter, James R.↗
NASA NTRS2008

Construction of Penrose Diagrams for Dynamic Black Holes

A set of Penrose diagrams is constructed in order to examine the large-scale causal structure of black holes with dynamic horizons. Coordinate dependencies of significant features, such as the event horizon and radial mass scale, are demonstrated on the diagrams. Unlike in static Schwarzschild geometries, the radial mass scale is clearly seen to differ from the horizon. Trajectories for photons near the horizon are briefly discussed.

Brown, Beth A.↗
NASA NTRS2007

Construction of a Penrose Diagram for a Spatially Coherent Evaporating Black Hole

A Penrose diagram is constructed for an example black hole that evaporates at a steady rate as measured by a distant observer, until the mass vanishes, yielding a final state Minkowski space-time. Coordinate dependencies of significant features, such as the horizon and coordinate anomalies, are clearly demonstrated on the diagram. The large-scale causal structure of the space-time is briefly discussed.

Brown, Beth A.↗
NASA NTRS2021

Non-Causal Controller Approximation Methodology Augmented with Model Reference Control – Robust Design

This paper describes the development of a new control design methodology based on a non-causal controller structure and its approximation. The non-causal controller design is based on a loop shaping approach. Partial fraction decomposition is used, along with derivative approximations, to derive a proper transfer function design for a causal control structure. The premise for this non-causal control methodology is that the phase delay as a function of frequency ideally remains within approximately -90o, which tends to make the system robustly stable to uncertainties in the plant dynamics. To have a predictable control system response in lieu of the tolerance of this methodology to relatively large uncertainty in the plant dynamics, the approach is augmented with model reference control. The addition of model reference control can potentially further enhance the robustness of the design. A control system design example is presented in this paper, along with simulation results, to demonstrate the high degree of robustness of this non-causal methodology and its augmentation.

Control Systems↗
NASA NTRS1988

Knowledge acquisition for autonomous systems

Knowledge-based capabilities for autonomous aerospace systems, such as the NASA Space Station, must encompass conflict-resolution functions comparable to those of human operators, with all elements of the system working toward system goals in a concurrent, asynchronous-but-coordinated fashion. Knowledge extracted from a design database will support robotic systems by furnishing geometric, structural, and causal descriptions required for repair, disassembly, and assembly. The factual knowledge for these databases will be obtained from a master database through a technical management information system, and it will in many cases have to be augmented by domain-specific heuristic knowledge acquired from domain experts.

Lum, Henry↗
NASA NTRSDate not supplied

A Method for Validating Causal Diagrams of Human Health Risk in Space Flight

The complexity of cause-and-effect relationships between spaceflight hazards and resulting health conditions clouds understanding of the totality of human system risk in space. In response, NASA has introduced Directed Acyclic Graphs (causal diagrams) into the human systems risk management process. These diagrams allow for a common understanding of the mechanisms that lead from unique hazards of spaceflight to the health outcomes important to agencies and astronauts. However, the paucity of available biomedical data from spaceflight creates a need for methods of validating causal models that can accommodate data from spaceflight model analogs. Here we outline one approach utilizing open-access rodent bone datasets from the Ames Life Sciences Data Archive. The properties of directed acyclic graphs themselves can provide an epistemological and statistical framework for validation of a priori causal representations of human system risk in space flight. The assumed causal connections on the graph creates sets of logical implications: variables that – if the causal diagram is correct – should be correlated, as well as sets that should be conditionally independent. By testing these implied correlations and conditional independencies both statistically and heuristically, we can provide evidence for or against specific causal pathways on the causal diagram. In addition to validation of expert-generated causal diagrams, machine learning techniques can learn the most likely structure of a causal diagram from a given dataset. Comparison with and reconciliation between machine-learned causal diagrams and expert-generated diagrams is another technique for challenging assumptions and improving our understanding of causal mechanisms. Accurately representing complex causation is essential to systemic understanding of human health risks in space travel. Having a robust system of validating causal diagrams helps us arrive at more accurate representations of causal systems. This process will be integral to developing the countermeasures necessary for extended exploration of the moon and Mars.

Robert Reynolds↗
NASA NTRS1992

An architecture for the development of real-time fault diagnosis systems using model-based reasoning

Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

Hall, Gardiner A.↗
NASA NTRS1993

Case-based approach to handling aircraft malfunctions

Reasoning about physical systems in operation is a difficult task, and any attempt to automate the process must overcome the problems of modeling normal behavior, diagnosing faults, and predicting future behavior. This paper describes a prototypical case-based reasoner (CBR) that operates in the domain of in-flight fault diagnosis and prognosis of aviation subsystems, particularly jet engines. The reasoner operates on the observation that the ability of a CBR program to reason about physical systems can be significantly enhanced by the addition to the CBR program of a model of the physical system to describe the system's structural, functional, and causal behavior.

Karamouzis, Stamos T.↗
NASA NTRS2007

Productivity Analysis of Public and Private Airports: A Causal Investigation

Around the world, airports are being viewed as enterprises, rather than public services, which are expected to be managed efficiently and provide passengers with courteous customer services. Governments are, increasingly, turning to the private sectors for their efficiency in managing the operation, financing, and development, as well as providing security for airports. Operational and financial performance evaluation has become increasingly important to airport operators due to recent trends in airport privatization. Assessing performance allows the airport operators to plan for human resources and capital investment as efficiently as possible. Productivity measurements may be used as comparisons and guidelines in strategic planning, in the internal analysis of operational efficiency and effectiveness, and in assessing the competitive position of an airport in transportation industry. The primary purpose of this paper is to investigate the operational and financial efficiencies of 22 major airports in the United States and Europe. These airports are divided into three groups based on private ownership (7 British Airport Authority airports), public ownership (8 major United States airports), and a mix of private and public ownership (7 major European Union airports. The detail ownership structures of these airports are presented in Appendix A. Total factor productivity (TFP) model was utilized to measure airport performance in terms of financial and operational efficiencies and to develop a benchmarking tool to identify the areas of strength and weakness. A regression model was then employed to measure the relationship between TFP and ownership structure. Finally a Granger causality test was performed to determine whether ownership structure is a Granger cause of TFP. The results of the analysis presented in this paper demonstrate that there is not a significant relationship between airport TFP and ownership structure. Airport productivity and efficiency is, however dependent upon the level of competition, choice of the market, and regulatory control.

Vasigh, Bijan↗
NASA NTRS1991

2-D nonlinear IIR-filters for image processing - An exploratory analysis

A new nonlinear IIR filter structure is introduced and its deterministic properties are analyzed. It is shown to be better suited for image processing applications than its linear shift-invariant counterpart. The new structure is obtained from causality inversion of a 2D quarterplane causal linear filter with respect to the two directions of propagation. It is demonstrated, that by using this design, a nonlinear 2D lowpass filter can be constructed, which is capable of effectively suppressing Gaussian or impulse noise without destroying important image information.

Bauer, P. H.↗
NASA NTRS1988

A knowledge-based approach to identification and adaptation in dynamical systems control

Artificial intelligence techniques are applied to the problems of model form and parameter identification of large-scale dynamic systems. The object-oriented knowledge representation is discussed in the context of causal modeling and qualitative reasoning. Structured sets of rules are used for implementing qualitative component simulations, for catching qualitative discrepancies and quantitative bound violations, and for making reconfiguration and control decisions that affect the physical system. These decisions are executed by backward-chaining through a knowledge base of control action tasks. This approach was implemented for two examples: a triple quadrupole mass spectrometer and a two-phase thermal testbed. Results of tests with both of these systems demonstrate that the software replicates some or most of the functionality of a human operator, thereby reducing the need for a human-in-the-loop in the lower levels of control of these complex systems.

Glass, B. J.↗
NASA NTRS2020

A Novel Parsing-based Approach for Verification of Hierarchical Plans

Hierarchical Task Networks were proposed as amethod to describe plans by decomposition of tasks to subtasksuntil primitive tasks, actions, are obtained. Valid plans –sequences of actions – must adhere both to causal dependenciesbetween the actions and to the structure given by the decompositionof the goal task. Plan verification aims at finding if a givenplan is valid, that is, if it is causally consistent and it can beobtained by decomposition of some task. The paper describes anovel parsing-based approach for hierarchical plan verificationthat is orders of magnitude faster than existing methods.

Bercher, Pascal↗