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Results for “logical reasoning”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

FuzzObserver

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Howard, Ayanna↗
NASA NTRS1997

Logic Design Pathology and Space Flight Electronics

Logic design errors have been observed in space flight missions and the final stages of ground test. The technologies used by designers and their design/analysis methodologies will be analyzed. This will give insight to the root causes of the failures. These technologies include discrete integrated circuit based systems, systems based on field and mask programmable logic, and the use computer aided engineering (CAE) systems. State-of-the-art (SOTA) design tools and methodologies will be analyzed with respect to high-reliability spacecraft design and potential pitfalls are discussed. Case studies of faults from large expensive programs to "smaller, faster, cheaper" missions will be used to explore the fundamental reasons for logic design problems.

Katz, Richard↗
DOE OSTI2024

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

Ontologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.We assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.These findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1992

Fuzzy logic control for camera tracking system

A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

Lea, Robert N.↗
NASA NTRSDate not supplied

Runtime Analysis with R2U2: A Tool Exhibition Report

We present R2U2 (Realizable, Responsive, Unobtrusive Unit), a hardware- supported tool and framework for the continuous monitoring of safetycritical and embedded cyber-physical systems.With the widespread advent of autonomous systems such as Unmanned Aerial Systems (UAS), satellites, rovers, and cars, real-time, on-board decision making requires unobtrusive monitoring of properties for safety, performance, security, and system health. R2U2 models combine past-time and future-time Metric Temporal Logic, “mission time” Linear Temporal Logic, probabilistic reasoning with Bayesian Networks, and modelbased prognostics. The R2U2 monitoring engine can be instantiated as a hardware solution, running on an FPGA, or as a software component. The FPGA realization enables R2U2 to monitor complex cyber-physical systems without any overhead or instrumentation of the flight software. In this tool exhibition report, we present R2U2 and demonstrate applications on system runtime monitoring, diagnostics, software health management, and security monitoring for a UAS. Our tool demonstration uses a hardware-based processor-in-the-loop “iron-bird” configuration.

Johann Martin Schumann↗
NASA NTRSDate not supplied

Usability of an Updated Version of the Supplemental Data Service Provider-Consolidated Dashboard for Supporting Uncrewed Aircraft System Traffic Management

The Supplemental Data Service Provider-Consolidated Dashboard (SDSP-CD) is a preflight planning user interface (UI) that serves to aid operators when drafting routes for small uncrewed aircraft systems (sUASs). The primary function of the SDSP-CD is to identify hazards that an sUAS may encounter along a proposed flight path and assess the severity of these risks. A usability study was conducted on an updated version of the SDSP-CD to determine if the most recent iterations made to the system improved objective performance and subjective user experience. There are two main components of the SDSP-CD interface: (1) the dashboard and (2) the interactive map. The dashboard provides users with hazard and vehicle limitations for each sUAS in their fleet while the map contains a graphical representation of each vehicle’s route, hazard details, and geographic information. A series of preflight risk-assessment questions and tasks were developed to examine how participants interact with the updated version of the SDSP-CD. Additionally, a new service that measures vertiport congestion was developed and included as one of the services that was tested. In the present study, participants were trained to use the SDSP-CD and then completed two simulated scenarios during which they performed a variety of tasks, responded to questions, and completed surveys. The two scenarios developed for the present study were the Package Delivery and Hurricane Preparation scenarios. The Package Delivery scenario involved a fleet of four sUASs delivering low-stakes items (e.g., lunches and snacks) to people in a fictitious city. The Hurricane Preparation scenario involved a fleet of 11 sUASs delivering a range of supplies (from medicine to boardgames) to employees stranded at an office park due to road closures caused by an impending hurricane. Participants assumed the role of a fleet manager during both scenarios and were responsible for managing the sUASs in their fleet. Questions included those with objectively correct responses, open-ended strategy responses, and subjective user experience feedback. It was found that participants were largely successful at using the SDSP-CD interface to answer questions with objectively correct responses. Additionally, participants were able to use reasoning and logic based on the information available in the SDSP-CD to determine the cause of various risks and what actions they would consider taking. Finally, although participants reported that there were elements of the UI that could be improved, overall feedback pertaining to user experience suggested that the SDSP-CD concept is viable.

usability testing↗
NASA NTRS1991

Reasoning about real-time systems with temporal interval logic constraints on multi-state automata

Models of real-time systems using a single paradigm often turn out to be inadequate, whether the paradigm is based on states, rules, event sequences, or logic. A model-based approach to reasoning about real-time systems is presented in which a temporal interval logic called TIL is employed to define constraints on a new type of high level automata. The combination, called hierarchical multi-state (HMS) machines, can be used to model formally a real-time system, a dynamic set of requirements, the environment, heuristic knowledge about planning-related problem solving, and the computational states of the reasoning mechanism. In this framework, mathematical techniques were developed for: (1) proving the correctness of a representation; (2) planning of concurrent tasks to achieve goals; and (3) scheduling of plans to satisfy complex temporal constraints. HMS machines allow reasoning about a real-time system from a model of how truth arises instead of merely depending of what is true in a system.

Gabrielian, Armen↗
NASA NTRS1988

Mathematical algorithms for approximate reasoning

Most state of the art expert system environments contain a single and often ad hoc strategy for approximate reasoning. Some environments provide facilities to program the approximate reasoning algorithms. However, the next generation of expert systems should have an environment which contain a choice of several mathematical algorithms for approximate reasoning. To meet the need for validatable and verifiable coding, the expert system environment must no longer depend upon ad hoc reasoning techniques but instead must include mathematically rigorous techniques for approximate reasoning. Popular approximate reasoning techniques are reviewed, including: certainty factors, belief measures, Bayesian probabilities, fuzzy logic, and Shafer-Dempster techniques for reasoning. A group of mathematically rigorous algorithms for approximate reasoning are focused on that could form the basis of a next generation expert system environment. These algorithms are based upon the axioms of set theory and probability theory. To separate these algorithms for approximate reasoning various conditions of mutual exclusivity and independence are imposed upon the assertions. Approximate reasoning algorithms presented include: reasoning with statistically independent assertions, reasoning with mutually exclusive assertions, reasoning with assertions that exhibit minimum overlay within the state space, reasoning with assertions that exhibit maximum overlay within the state space (i.e. fuzzy logic), pessimistic reasoning (i.e. worst case analysis), optimistic reasoning (i.e. best case analysis), and reasoning with assertions with absolutely no knowledge of the possible dependency among the assertions. A robust environment for expert system construction should include the two modes of inference: modus ponens and modus tollens. Modus ponens inference is based upon reasoning towards the conclusion in a statement of logical implication, whereas modus tollens inference is based upon reasoning away from the conclusion. These algorithms allow one to reason accurately with uncertain data. The above environment can replicate state-f-the-art expert system environments which provides a continuity between the current expert systems which cannot be validated or verified and future expert systems which should be both validated and verified

Murphy, John H.↗
NASA NTRS1988

Approximate spatial reasoning

A model for approximate spatial reasoning using fuzzy logic to represent the uncertainty in the environment is presented. Algorithms are developed which can be used to reason about spatial information expressed in the form of approximate linguistic descriptions similar to the kind of spatial information processed by humans. Particular attention is given to static spatial reasoning.

Dutta, Soumitra↗
NASA NTRS1989

Stress in dilute suspensions

Generally, two types of theory are used to describe the field equations for suspensions. The so-called postulated equations are based on the kinetic theory of mixtures, which logically should give reasonable equations for solutions. The basis for the use of such theory for suspensions is tenuous, though it at least gives a logical path for mathematical arguments. It has the disadvantage that it leads to a system of equations which is underdetermined, in a sense that can be made precise. On the other hand, the so-called averaging theory starts with a determined system, but the very process of averaging renders the resulting system underdetermined. A third type of theory is proposed in which the kinetic theory of gases is used to motivate continuum equations for the suspended particles. This entails an interpretation of the stress in the particles that is different from the usual one. Classical theory is used to describe the motion of the suspending medium. The result is a determined system for a dilute suspension. Extension of the theory to more concentrated systems is discussed.

Passman, Stephen L.↗
NASA NTRS1991

GLOBEC: Global Ocean Ecosystems Dynamics: A component of the US Global Change Research Program

GLOBEC (GLOBal ocean ECosystems dynamics) is a research initiative proposed by the oceanographic and fisheries communities to address the question of how changes in global environment are expected to affect the abundance and production of animals in the sea. The approach to this problem is to develop a fundamental understanding of the mechanisms that determine both the abundance of key marine animal populations and their variances in space and time. The assumption is that the physical environment is a major contributor to patterns of abundance and production of marine animals, in large part because the planktonic life stages typical of most marine animals are intrinsically at the mercy of the fluid motions of the medium in which they live. Consequently, the authors reason that a logical approach to predicting the potential impact of a globally changing environment is to understand how the physical environment, both directly and indirectly, contributes to animal abundance and its variability in marine ecosystems. The plans for this coordinated study of of the potential impact of global change on ocean ecosystems dynamics are discussed.

Source record↗
NASA NTRS1994

A statistical inference approach for the retrieval of the atmospheric ozone profile from simulated satellite measurements of solar backscattered ultraviolet radiation

NASA's Mission to Planet Earth (MTPE) will address important interdisciplinary and environmental issues such as global warming, ozone depletion, deforestation, acid rain, and the like with its long term satellite observations of the Earth and with its comprehensive Data and Information System. Extensive sets of satellite observations supporting MTPE will be provided by the Earth Observing System (EOS), while more specific process related observations will be provided by smaller Earth Probes. MTPE will use data from ground and airborne scientific investigations to supplement and validate the global observations obtained from satellite imagery, while the EOS satellites will support interdisciplinary research and model development. This is important for understanding the processes that control the global environment and for improving the prediction of events. In this paper we illustrate the potential for powerful artificial intelligence (AI) techniques when used in the analysis of the formidable problems that exist in the NASA Earth Science programs and of those to be encountered in the future MTPE and EOS programs. These techniques, based on the logical and probabilistic reasoning aspects of plausible inference, strongly emphasize the synergetic relation between data and information. As such, they are ideally suited for the analysis of the massive data streams to be provided by both MTPE and EOS. To demonstrate this, we address both the satellite imagery and model enhancement issues for the problem of ozone profile retrieval through a method based on plausible scientific inferencing. Since in the retrieval problem, the atmospheric ozone profile that is consistent with a given set of measured radiances may not be unique, an optimum statistical method is used to estimate a 'best' profile solution from the radiances and from additional a priori information.

Bonavito, N. L.↗
NASA NTRS2018

On-Board Battery Monitoring and Prognostics for Electric-Propulsion Aircraft

The reliability of the propulsion system of an aircraft is paramount for the aircraft safety and hence the aircraft health must be monitored continuously. In contrast to fuel- operated aircraft, electric battery-operated propulsion system poses specific problems, such as, the remaining battery power does not linearly decrease and cannot be measured directly. In this paper, we describe a combined monitoring and prognostics architecture that can continuously monitor all components of the electric propulsion system with respect to safety and performance properties as well as state of charge and rest of useful life for the battery. Our system combines a detailed electrochemical battery model for Li-ion batteries with a powerful prognostics engine based upon an Unscented Kalman Filter with the R2U2 monitoring device, which provides efficient observers for metric temporal logic and Bayesian reasoning. R2U2 is a real-time, realizable, responsive, unobtrusive unit, which continuously monitors sensor readings, outputs of the prognostics engine, as well as the ight software status for safety, performance, and security properties. We illustrate our architecture with two case studies, one reporting actual ight tests with an X8+ octocopter and the other a software-in-the-loop simulation with an unmanned Edge 540 electric aircraft model.

Kulkarni, Chetan↗
NASA NTRS1990

The Temporal Logic of the Tower Chief System

The purpose is to describe the logic used in the reasoning scheme employed in the Tower Chief system, a runway configuration management system. First, a review of classical logic is given. Defensible logics, truth maintenance, default logic, temporally dependent propositions, and resource allocation and planning are discussed.

Hazelton, Lyman R., Jr.↗
NASA NTRS1991

A General Framework for Modal Deduction

A general method of automated modal logic theorem proving is discussed and illustrated. This method is based on the substitutional framework for the development of systems for hybrid reasoning. Sentences in modal logic are translated into a constraint logic in which the constraints represent the connections between worlds in the possible world semantics for modal logic. Deduction in the constraint logic is performed by a non-modal deductive system which has been systematically enhanced with special-purpose constraint processing mechanisms. The result is a modal logic theorem prover, whose soundness and completeness is an immediate consequence of the correctness of the non-modal deductive system and some general results on constraint deduction. The framework achieves significant generality in that it provides for the extension of a wide range of non-modal systems to corresponding modal systems and that can be done for a wide range of modal logics.

Frisch, Alan M.↗
NASA NTRS2018

Efficient Runtime Verification of First-Order Temporal Properties

Runtime verification allows monitoring the execution of a system against a temporal property, raising an alarm if the property is violated. In this paper we present a theory and system for runtime verification of a first-order past time linear temporal logic. The first-order nature of the logic allows a monitor to reason about events with data elements. While runtime verification of propositional temporal logic requires only a fixed amount of memory, the first-order variant has to deal with a number of data values potentially growing unbounded in the length of the execution trace. This requires special compactness considerations in order to allow checking very long executions. In previous work we presented an efficient use of BDDs for such first-order runtime verification, implemented in the tool DEJAVU. We first summarize this previous work. Subsequently, we look at the new problem of dynamically identifying when data observed in the past are no longer needed, allowing to reclaim the data elements used to represent them. We also study the problem of adding relations over data values. Finally, we present parts of the implementation, including a new concept of user defined property macros.

Peled, Doron↗