Warm-phase spectral-bin microphysics in ICON: reasons of sensitivity to aerosols
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54 ENVIRONMENTAL SCIENCES↗
SEARCH · Engineering Papers
Results for “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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ReTA: Recursively Thinking Ahead to Improve the Strategic Reasoning of Large Language Models
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Computer science↗
From Machine Learning to Machine Reasoning: A Model-Based System Engineering Perspective
Presentation for DICE conference
97 MATHEMATICS AND COMPUTING↗
MORAL JUDGEMENT IN STEM STUDENTS AND PROFESSIONALS: DEVELOPMENT AND VALIDATION OF THE GENERAL ENGINEERING ETHICS REASONING INSTRUMENT (GEERI)
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Odom, Peter Wesley [Sandia National Laboratories (↗
Constructing Simplified Plans via Truth Criteria Approximation
This paper has presented an approach to dealing with the complexity of explanation-based learning plans in complex domains. This approach uses a simplified algorithm to construct plans, and employs later refinements to repair bugs in constructed plans. This algorithm has the theoretical properties of completeness and convergence upon soundness. This incremental reasoning planning and learning algorithm has been implemented using a partial-order constraint posting planner and empirically compared to a conventional exhaustive reasoning partial-order constraint-posting planner and learning algorithm. This comparison showed that 1) incremental reasoning significantly reduced learning costs compared to exhaustive reasoning, 2) Explanation-based Learning (EBL) reduced failures from incremental reasoning, 3) EBL with incremental reasoning required less search to solve problems than EBL with exhaustive reasoning.
Explanation-based learning EBL incremental reasoni↗
Three-color resonance ionization spectroscopy of Zr in Si
As our society becomes technologically more complex, computers are being used in greater and greater numbers of high consequence systems. Giving a machine control over the lives of humans can be disturbing, especially if the software that is run on such a machine has bugs. Formal reasoning is one of the most powerful techniques available to demonstrate the correctness of a piece of software. When reasoning about software and its development, one frequently encounters expressions that contain partial functions. As might be expected, the presence of partial functions introduces an additional dimension of difficulty to the reasoning framework. This difficulty produces an especially strong impact in the case of high consequence systems. An ability to use formal methods for constructing software is essential if we want to obtain greater confidence in such systems through formal reasoning. This is only reasonable under automation of software development and verification. However, the ubiquitous presence of partial functions prevents a uniform application to software of any tools not specifically accounting for partial functions. In this paper we will describe a framework for reasoning about software, based on the nonstrict explicit domain approach, that is applicable to a large class of software/hardware systems. In this framework the Hoare triples containing partial functions can be reasoned about automatically in a well-defined and uniform manner.
Hansen, C. S.↗
The use of multiple models in case-based diagnosis
The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation.
Karamouzis, Stamos T.↗
A study of mapping exogenous knowledge representations into CONFIG
Qualitative reasoning is reasoning with a small set of qualitative values that is an abstraction of a larger and perhaps infinite set of quantitative values. The use of qualitative and quantitative reasoning together holds great promise for performance improvement in applications that suffer from large and/or imprecise knowledge domains. Included among these applications are the modeling, simulation, analysis, and fault diagnosis of physical systems. Several research groups continue to discover and experiment with new qualitative representations and reasoning techniques. However, due to the diversity of these techniques, it is difficult for the programs produced to exchange system models easily. The availability of mappings to transform knowledge from the form used by one of these programs to that used by another would open the doors for comparative analysis of these programs in areas such as completeness, correctness, and performance. A group at the Johnson Space Center (JSC) is working to develop CONFIG, a prototype qualitative modeling, simulation, and analysis tool for fault diagnosis applications in the U.S. space program. The availability of knowledge mappings from the programs produced by other research groups to CONFIG may provide savings in CONFIG's development costs and time, and may improve CONFIG's performance. The study of such mappings is the purpose of the research described in this paper. Two other research groups that have worked with the JSC group in the past are the Northwest University Group and the University of Texas at Austin Group. The former has produced a qualitative reasoning tool named SIMGEN, and the latter has produced one named QSIM. Another program produced by the Austin group is CC, a preprocessor that permits users to develop input for eventual use by QSIM, but in a more natural format. CONFIG and CC are both based on a component-connection ontology, so a mapping from CC's knowledge representation to CONFIG's knowledge representation was chosen as the focus of this study. A mapping from CC to CONFIG was developed. Due to differences between the two programs, however, the mapping transforms some of the CC knowledge to CONFIG as documentation rather than as knowledge in a form useful to computation. The study suggests that it may be worthwhile to pursue the mappings further. By implementing the mapping as a program, actual comparisons of computational efficiency and quality of results can be made between the QSIM and CONFIG programs. A secondary study may reveal that the results of the two programs augment one another, contradict one another, or differ only slightly. If the latter, the qualitative reasoning techniques may be compared in other areas, such as computational efficiency.
Mayfield, Blayne E.↗
A novel tiered sensor fusion approach for terrain characterization and safe landing assessment
This paper presents a novel tiered sensor fusion methodology for real-time terrain safety assessment. A combination of active and passive sensors, specifically, radar, lidar, and camera, operate in three tiers according to their inherent ranges of operation. Low-level terrain features (e.g. slope, roughness) and high-level terrain features (e.g. hills, craters) are integrated using principles of reasoning under uncertainty. Three methodologies are used to infer landing safety: Fuzzy Reasoning, Probabilistic Reasoning, and Evidential Reasoning. The safe landing predictions from the three fusion engines are consolidated in a subsequent decision fusion stage aimed at combining the strengths of each fusion methodology. Results from simulated spacecraft descents are presented and discussed.
fuzzy reasoning↗
py-boomer v0.1.0
Py-BOOMER (Python Bayesian OWL Ontology MErgER in Python) is a probabilistic reasoning system for knowledge representation and ontological reasoning with uncertainty. Itnables reasoning over probabilistic facts and taxonomic relationships, finding the most likely consistent interpretation of potentially conflicting assertions. It uses a combination of graph-based reasoning and Bayesian probabilistic inference. Key features: Represent probabilistic ontological statements Reason over class subsumption hierarchies Evaluate class equivalence relationships Detect and resolve logical inconsistencies Calculate posterior probabilities for each assertion
Mungall, Chris [Lawrence Berkeley National Laborat↗
Guidelines for Beamline and Front-End Radiation Shielding Design at the Advanced Photon Source (Rev. 5)
Shielding for the APS will be such that the individual radiation worker dose will be as low as reasonably achievable (ALARA). The ALARA goals for the APS are to keep the total of the work-related radiation exposure (exposure coming from other than natural or medical sources) as far below 5 person mSv/yr (500 person-mrem/yr), collective total effective dose equivalent, as reasonably achievable. For an individual APS radiation worker, the goal is to keep the maximum occupational total effective dose equivalent of any one employee as far below 2 mSv/yr as reasonably achievable. The ALARA goal for APS beamline scientists is to keep the total of the work-related radiation exposure (exposure coming from other than natural or medical sources) as far below 1 person mSv/yr (100 person-mrem/yr), collective total effective dose equivalent, as reasonably achievable. For an individual APS beamline scientist, the goal is to keep the maximum occupational total effective dose equivalent of any one scientist as far below 0.5 mSv/yr (50 mrem/yr) as reasonably achievable. The dose is actively monitored by the radiation monitors on the storage ring wall in each sector and by the frequent area surveys performed by the health physics personnel. For cases in which surveys indicate elevated hourly dose rates that may impact worker exposure, additional local shielding is provided to reduce the radiation field to an acceptable level. Passive area monitors are used throughout the facility to integrate doses in various areas. The results are analyzed for trends of increased doses, and shielding in these areas is evaluated and improved, as appropriate. The APS policy for on-site nonradiation workers in the vicinity of the APS facilities requires that the average nonradiation worker dose be below 0.2 mSv/yr (20 mrem/yr). In addition, the dose at the site boundary from all pathways is required to be below 0.1 mSv/yr (10 mrem/yr). For future modifications of the facility, the doses shall be evaluated and additional shielding provided to meet the policy requirements.
61 RADIATION PROTECTION AND DOSIMETRY↗
Diagnostics in the Extendable Integrated Support Environment (EISE)
Extendable Integrated Support Environment (EISE) is a real-time computer network consisting of commercially available hardware and software components to support systems level integration, modifications, and enhancement to weapons systems. The EISE approach offers substantial potential savings by eliminating unique support environments in favor of sharing common modules for the support of operational weapon systems. An expert system is being developed that will help support diagnosing faults in this network. This is a multi-level, multi-expert diagnostic system that uses experiential knowledge relating symptoms to faults and also reasons from structural and functional models of the underlying physical model when experiential reasoning is inadequate. The individual expert systems are orchestrated by a supervisory reasoning controller, a meta-level reasoner which plans the sequence of reasoning steps to solve the given specific problem. The overall system, termed the Diagnostic Executive, accesses systems level performance checks and error reports, and issues remote test procedures to formulate and confirm fault hypotheses.
Brink, James R.↗
An architecture for object-oriented intelligent control of power systems in space
A control system for autonomous distribution and control of electrical power during space missions is being developed. This system should free the astronauts from localizing faults and reconfiguring loads if problems with the power distribution and generation components occur. The control system uses an object-oriented simulation model of the power system and first principle knowledge to detect, identify, and isolate faults. Each power system component is represented as a separate object with knowledge of its normal behavior. The reasoning process takes place at three different levels of abstraction: the Physical Component Model (PCM) level, the Electrical Equivalent Model (EEM) level, and the Functional System Model (FSM) level, with the PCM the lowest level of abstraction and the FSM the highest. At the EEM level the power system components are reasoned about as their electrical equivalents, e.g, a resistive load is thought of as a resistor. However, at the PCM level detailed knowledge about the component's specific characteristics is taken into account. The FSM level models the system at the subsystem level, a level appropriate for reconfiguration and scheduling. The control system operates in two modes, a reactive and a proactive mode, simultaneously. In the reactive mode the control system receives measurement data from the power system and compares these values with values determined through simulation to detect the existence of a fault. The nature of the fault is then identified through a model-based reasoning process using mainly the EEM. Compound component models are constructed at the EEM level and used in the fault identification process. In the proactive mode the reasoning takes place at the PCM level. Individual components determine their future health status using a physical model and measured historical data. In case changes in the health status seem imminent the component warns the control system about its impending failure. The fault isolation process uses the FSM level for its reasoning base.
Holmquist, Sven G.↗
The Logic of Reachability
In recent years, Graphplan style reachability analysis and mutual exclusion reasoning have been used in many high performance planning systems. While numerous refinements and extensions have been developed, the basic plan graph structure and reasoning mechanisms used in these systems are tied to the very simple STRIPS model of action. In 1999, Smith and Weld generalized the Graphplan methods for reachability and mutex reasoning to allow actions to have differing durations. However, the representation of actions still has some severe limitations that prevent the use of these techniques for many real-world planning systems. In this paper, we 1) separate the logic of reachability from the particular representation and inference methods used in Graphplan, and 2) extend the notions of reachability and mutual exclusion to more general notions of time and action. As it turns out, the general rules for mutual exclusion reasoning take on a remarkably clean and simple form. However, practical instantiations of them turn out to be messy, and require that we make representation and reasoning choices.
Smith, David E.↗
Towards a Compositional SPIN
This paper discusses our initial experience with introducing automated assume-guarantee verification based on learning in the SPIN tool. We believe that compositional verification techniques such as assume-guarantee reasoning could complement the state-reduction techniques that SPIN already supports, thus increasing the size of systems that SPIN can handle. We present a "light-weight" approach to evaluating the benefits of learning-based assume-guarantee reasoning in the context of SPIN: we turn our previous implementation of learning for the LTSA tool into a main program that externally invokes SPIN to provide the model checking-related answers. Despite its performance overheads (which mandate a future implementation within SPIN itself), this approach provides accurate information about the savings in memory. We have experimented with several versions of learning-based assume guarantee reasoning, including a novel heuristic introduced here for generating component assumptions when their environment is unavailable. We illustrate the benefits of learning-based assume-guarantee reasoning in SPIN through the example of a resource arbiter for a spacecraft. Keywords: assume-guarantee reasoning, model checking, learning.
Pasareanu, Corina S.↗
Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis
Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking.
Ong, James C.↗
A Temporal Differential Dynamic Logic Formal Embedding
Differential dynamic logic is a formal framework to specify and reason about hybrid programs (HPs). The core of dL is a proof calculus that contains a collection of axioms and rules for the rigorous verification of properties of HPs. Recently, dL has been embedded within the theorem prover Prototype Verification System (PVS) resulting in the tool Plaidypvs2. The integration of dL into PVS expands its expressive power; user defined functions, such as trigonometric and other transcendental functions, can be used inside the dL framework, and meta-reasoning about HPs can be performed, including reasoning about entire classes of HPs, specified using dependent types in PVS. The differential temporal dynamic logic (dTL2) extends dL with temporal logic operators to reason about all the states reachable during the execution of an HP. This paper presents a work in progress focusing on embedding dTL2 in PVS as an extension of Plaidypvs. Plaidypvs is expanded with the formalization of a trace semantics for HPs, the definition of the LTL temporal operators eventually and globally, and the implementation of the proof calculus for dTL2. This new embedding has the same capabilties as Plaidypvs, which allows user defined functions and meta-reasoning of properties of HPs. To the best of the authors’ knowledge this is the first implementation of dTL2.
differential dynamic logic↗