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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 73 records · Page 4

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NASA NTRS1990

CLIPS: A tool for corn disease diagnostic system and an aid to neural network for automated knowledge acquisition

This paper describes the building of a corn disease diagnostic expert system using CLIPS, and the development of a neural expert system using the fact representation method of CLIPS for automated knowledge acquisition. The CLIPS corn expert system diagnoses 21 diseases from 52 symptoms and signs with certainty factors. CLIPS has several unique features. It allows the facts in rules to be broken down to object-attribute-value (OAV) triples, allows rule-grouping, and fires rules based on pattern-matching. These features combined with the chained inference engine result to a natural user query system and speedy execution. In order to develop a method for automated knowledge acquisition, an Artificial Neural Expert System (ANES) is developed by a direct mapping from the CLIPS system. The ANES corn expert system uses the same OAV triples in the CLIPS system for its facts. The LHS and RHS facts of the CLIPS rules are mapped into the input and output layers of the ANES, respectively; and the inference engine of the rules is imbedded in the hidden layer. The fact representation by OAC triples gives a natural grouping of the rules. These features allow the ANES system to automate rule-generation, and make it efficient to execute and easy to expand for a large and complex domain.

Wu, Cathy↗

NASA NTRS2010

Automated Computer Access Request System

The Automated Computer Access Request (AutoCAR) system is a Web-based account provisioning application that replaces the time-consuming paper-based computer-access request process at Johnson Space Center (JSC). Auto- CAR combines rules-based and role-based functionality in one application to provide a centralized system that is easily and widely accessible. The system features a work-flow engine that facilitates request routing, a user registration directory containing contact information and user metadata, an access request submission and tracking process, and a system administrator account management component. This provides full, end-to-end disposition approval chain accountability from the moment a request is submitted. By blending both rules-based and rolebased functionality, AutoCAR has the flexibility to route requests based on a user s nationality, JSC affiliation status, and other export-control requirements, while ensuring a user s request is addressed by either a primary or backup approver. All user accounts that are tracked in AutoCAR are recorded and mapped to the native operating system schema on the target platform where user accounts reside. This allows for future extensibility for supporting creation, deletion, and account management directly on the target platforms by way of AutoCAR. The system s directory-based lookup and day-today change analysis of directory information determines personnel moves, deletions, and additions, and automatically notifies a user via e-mail to revalidate his/her account access as a result of such changes. AutoCAR is a Microsoft classic active server page (ASP) application hosted on a Microsoft Internet Information Server (IIS).

Snook, Bryan E.↗

NASA NTRS1987

An evaluation of Turbo Prolog with an emphasis on its application to the development of expert systems

Turbo Prolog is a recently available, compiled version of the programming language Prolog. Turbo Prolog is designed to provide not only a Prolog compiler, but also a program development environment for the IBM Personal Computer family. An evaluation of Turbo Prolog was made, comparing its features to other versions of Prolog and to the community of languages commonly used in artificial intelligence (AI) research and development. Three programs were employed to determine the execution speed of Turbo Prolog applied to various problems. The results of this evaluation demonstrated that Turbo Prolog can perform much better than many commonly employed AI languages for numerically intensive problems and can equal the speed of development languages such as OPS5+ and CLIPS, running on the IBM PC. Applications for which Turbo Prolog is best suited include those which (1) lend themselves naturally to backward-chaining approaches, (2) require extensive use of mathematics, (3) contain few rules, (4) seek to make use of the window/color graphics capabilities of the IBM PC, and (5) require linkage to programs in other languages to form a complete executable image.

Loftin, Richard B.↗

NASA NTRS1986

The design and application of a Transportable Inference Engine (TIE1)

A Transportable Inference Engine (TIE1) system has been developed by the author as part of the Interactive Experimenter Planning System (IEPS) task which is involved with developing expert systems in support of the Spacecraft Control Programs Branch at Goddard Space Flight Center in Greenbelt, Maryland. Unlike traditional inference engines, TIE1 is written in the C programming language. In the TIE1 system, knowledge is represented by a hierarchical network of objects which have rule frames. The TIE1 search algorithm uses a set of strategies, including backward chaining, to obtain the values of goals. The application of TIE1 to a spacecraft scheduling problem is described. This application involves the development of a strategies interpreter which uses TIE1 to do constraint checking.

Mclean, David R.↗

NASA NTRS1986

The design and application of a Transportable Inference Engine (TIE1)

Mclean, David R.↗

NASA NTRS2022

Assurance of Electronic Parts for Aerospace System Reliability: Past, Present, and Future

Over the last two to three decades, the balance of influence within the electronics industry has shifted decisively towards the consumer electronics and information technology products, away from the defense or aerospace markets. This shift towards the commercial markets has changed certain ground rules in the electronics industry. Reliance of the aerospace industry, in particular, on components from supply chains dedicated to other products, or cross-vertical product integration, creates a supply web and introduces significant risks that are not necessarily present for volume-driven electronic products. Many of the unique risks in the aerospace industry emanate from the key characteristics of this industry. Although selection and usage of commercial electronics in NASA flight hardware is possible at a comparable (and in many cases lower) level of risk to that of employing parts that are screened to military or NASA specifications, for a large majority of flight projects it is difficult to distinguish the difference in on-orbit performance or reliability for parts screened at any level, or commercial parts that have no additional screening performed by the government. This paper identifies several bases for establishing context and developing unique or alternative approaches for successful parts selection and application within NASA and across the commercial space community.

electronic parts↗

NASA NTRS1986

An Ada inference engine for expert systems

The purpose is to investigate the feasibility of using Ada for rule-based expert systems with real-time performance requirements. This includes exploring the Ada features which give improved performance to expert systems as well as optimizing the tradeoffs or workarounds that the use of Ada may require. A prototype inference engine was built using Ada, and rule firing rates in excess of 500 per second were demonstrated on a single MC68000 processor. The knowledge base uses a directed acyclic graph to represent production lines. The graph allows the use of AND, OR, and NOT logical operators. The inference engine uses a combination of both forward and backward chaining in order to reach goals as quickly as possible. Future efforts will include additional investigation of multiprocessing to improve performance and creating a user interface allowing rule input in an Ada-like syntax. Investigation of multitasking and alternate knowledge base representations will help to analyze some of the performance issues as they relate to larger problems.

Lavallee, David B.↗

NASA NTRS1994

Using Commercial Off-the-Shelf Software Tools for Space Shuttle Scientific Software

In October 1993, the Astronaut Science Advisor (ASA) was on board the STS-58 flight of the space shuttle. ASA is an interactive system providing data acquisition and analysis, experiment step re-scheduling, and various other forms of reasoning. As fielded, the system runs on a single Macintosh PowerBook 170, which hosts the six ASA modules. There is one other piece of hardware, an external (GW Instruments, Sommerville, Massachusetts) analog-to-digital converter connected to the PowerBook's SCSI port. Three main software tools were used: LabVIEW, CLIPS, and HyperCard: First, a module written in LabVIEW (National Instruments, Austin, Texas) controls the A/D conversion and stores the resulting data in appropriate arrays. This module also analyzes the numerical data to produce a small set of characteristic numbers or symbols describing the results of an experiment trial. Second, a forward-chaining inference system written in CLIPS (NASA) uses the symbolic information provided by the first stage with a static rule base to infer decisions about the experiment. This expert system shell is used by the system for diagnosis. The third component of the system is the user interface, written in HyperCard (Claris Inc. and Apple Inc., both in Cupertino, California).

Groleau, Nicolas↗

NASA NTRS1994

Expert Witness: A system for developing expert medical testimony

Expert Witness in an expert system designed to assist attorneys and medical experts in determining the merit of medical malpractice claims in the area of obstetrics. It substitutes the time of the medical expert with the time of a paralegal assistant guided by the expert system during the initial investigation of the medical records and patient interviews. The product of the system is a narrative transcript containing important data, immediate conclusions from the data, and overall conclusions of the case that the attorney and medical expert use to make decisions about whether and how to proceed with the case. The transcript may also contain directives for gathering additional information needed for the case. The system is a modified heuristic classifier and is implemented using over 600 CLIPS rules together with a C-based user interface. The data abstraction and solution refinement are implemented directly using forward chaining production and matching. The use of CLIPS and C is essential to delivering a system that runs on a generic PC platform. The direct implementation in CLIPS together with locality of inference ensures that the system will scale gracefully. Two years of use has revealed no errors in the reasoning.

Lewandowski, Raymond↗

NASA NTRS1986

Crew Communication as a Factor in Aviation Accidents

The crew communication process is analyzed. Planning and explanation are shown to be well-structured discourse types, described by formal rules. These formal rules are integrated with those describing the other most important discourse type within the cockpit: the command-and-control speech act chain. The latter is described as a sequence of speech acts for making requests (including orders and suggestions), for making reports, for supporting or challenging statements, and for acknowledging previous speech acts. Mitigation level, a linguistic indication of indirectness and tentativeness in speech, was an important variable in several hypotheses, i.e., the speech of subordinates is more mitigated than the speech of superiors, the speech of all crewmembers is less mitigated when they know that they are in either a problem or emergency situation, and mitigation is a factor in failures of crewmembers to initiate discussion of new topics or have suggestions ratified by the captain. Test results also show that planning and explanation are more frequently performed by captains, are done more during crew- recognized problems, and are done less during crew-recognized emergencies. The test results also indicated that planning and explanation are more frequently performed by captains than by other crewmembers, are done more during crew-recognized problems, and are done less during-recognized emergencies.

Goguen, J.↗

NASA NTRS1988

Space station payload operations scheduling with ESP2

The Mission Analysis Division of the Systems Analysis and Integration Laboratory at the Marshall Space Flight Center is developing a system of programs to handle all aspects of scheduling payload operations for Space Station. The Expert Scheduling Program (ESP2) is the heart of this system. The task of payload operations scheduling can be simply stated as positioning the payload activities in a mission so that they collect their desired data without interfering with other activities or violating mission constraints. ESP2 is an advanced version of the Experiment Scheduling Program (ESP) which was developed by the Mission Integration Branch beginning in 1979 to schedule Spacelab payload activities. The automatic scheduler in ESP2 is an expert system that embodies the rules that expert planners would use to schedule payload operations by hand. This scheduler uses depth-first searching, backtracking, and forward chaining techniques to place an activity so that constraints (such as crew, resources, and orbit opportunities) are not violated. It has an explanation facility to show why an activity was or was not scheduled at a certain time. The ESP2 user can also place the activities in the schedule manually. The program offers graphical assistance to the user and will advise when constraints are being violated. ESP2 also has an option to identify conflict introduced into an existing schedule by changes to payload requirements, mission constraints, and orbit opportunities.

Stacy, Kenneth L.↗

NASA NTRS1986

Program for Experimentation With Expert Systems

CERBERUS is forward-chaining, knowledge-based system program useful for experimentation with expert systems. Inference-engine mechanism performs deductions according to user-supplied rule set. Information stored in intermediate area, and user interrogated only when no applicable data found in storage. Each assertion posed by CERBERUS answered with certainty ranging from 0 to 100 percent. Rule processor stops investigating applicable rules when goal reaches certainty of 95 percent or higher. Capable of operating for wide variety of domains. Sample rule files included for animal identification, pixel classification in image processing, and rudimentary car repair for novice mechanic. User supplies set of end goals or actions. System complexity decided by user's rule file. CERBERUS written in FORTRAN 77.

Engle, S. W.↗

NASA NTRS2005

PlanWorks: A Debugging Environment for Constraint Based Planning Systems

Numerous planning and scheduling systems employ underlying constraint reasoning systems. Debugging such systems involves the search for errors in model rules, constraint reasoning algorithms, search heuristics, and the problem instance (initial state and goals). In order to effectively find such problems, users must see why each state or action is in a plan by tracking causal chains back to part of the initial problem instance. They must be able to visualize complex relationships among many different entities and distinguish between those entities easily. For example, a variable can be in the scope of several constraints, as well as part of a state or activity in a plan; the activity can arise as a consequence of another activity and a model rule. Finally, they must be able to track each logical inference made during planning. We have developed PlanWorks, a comprehensive system for debugging constraint-based planning and scheduling systems. PlanWorks assumes a strong transaction model of the entire planning process, including adding and removing parts of the constraint network, variable assignment, and constraint propagation. A planner logs all transactions to a relational database that is tailored to support queries for of specialized views to display different forms of data (e.g. constraints, activities, resources, and causal links). PlanWorks was specifically developed for the Extensible Universal Remote Operations Planning Architecture (EUROPA(sub 2)) developed at NASA, but the underlying principles behind PlanWorks make it useful for many constraint-based planning systems. The paper is organized as follows. We first describe some fundamentals of EUROPA(sub 2). We then describe PlanWorks' principal components. We then discuss each component in detail, and then describe inter-component navigation features. We close with a discussion of how PlanWorks is used to find model flaws.

Daley, Patrick↗

NASA NTRSDate not supplied

Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods.

combinatorial reasoning↗

NASA NTRS1999

Molecular evidence of simian virus 40 infections in children

Recent studies have detected simian virus 40 (SV40) DNA in certain human tumors and normal tissues. The significance of human infections by SV40, which was first discovered as a contaminant of poliovirus vaccines used between 1955 and 1963, remains unknown. The occurrence of SV40 infections in unselected hospitalized children was evaluated. Polymerase chain reaction and DNA sequence analyses were done on archival tissue specimens from patients positive for SV40 neutralizing antibody. SV40 DNA was identified in samples from 4 of 20 children (1 Wilms' tumor, 3 transplanted kidney samples). Sequence variation among SV40 regulatory regions ruled out laboratory contamination of specimens. This study shows the presence of SV40 infections in pediatric patients born after 1982.

Non-NASA Center↗

NASA NTRS1987

A systems engineering approach to automated failure cause diagnosis in space power systems

Automatic failure-cause diagnosis is a key element in autonomous operation of space power systems such as Space Station's. A rule-based diagnostic system has been developed for determining the cause of degraded performance. The knowledge required for such diagnosis is elicited from the system engineering process by using traditional failure analysis techniques. Symptoms, failures, causes, and detector information are represented with structured data; and diagnostic procedural knowledge is represented with rules. Detected symptoms instantiate failure modes and possible causes consistent with currently held beliefs about the likelihood of the cause. A diagnosis concludes with an explanation of the observed symptoms in terms of a chain of possible causes and subcauses.

Dolce, James L.↗

NASA NTRSDate not supplied

Making & Breaking the Rules for DED Design

Design for Additive Manufacturing (DfAM) is widely used to describe design activities and guidelines employed in the creation of Additively Manufactured (AM) parts. Basic design rules for widely adopted processes like Powder Bed Fusion (PBF) are relatively understood including material performance, part size constraints, self-supporting angles, overhangs, wall thicknesses, and feature resolution. Complexity is often inherent with PBF using organically inspired solutions through generative design and topology optimization software to create designs that often cannot be realized via traditional manufacturing processes. The design rules for Directed Energy Deposition (DED) are less well known, and, in some cases, the lack of understanding is likely inhibiting broader application of the various DED processes. This presentation covers key design rules for DED fusion processes and elucidates opportunities like multi-material deposition and build approaches ranging from basic features to complex large-scale parts. Complementary to the opportunistic characteristics of DfAM for DED, Manufacture for AM (MfAM) encompasses the restrictive aspects of the manufacturing lifecycle with limitations due to bead width and wall thickness, self-supporting angles, surface texture, microstructural evolution, and residual stresses. AM enabled part design must always consider the entire value chain starting with incoming feedstock (e.g. plate, wire, or powder) and progressing through the printing process, post-processing (e.g. machining, inspection), and final system assembly. Key considerations, like the tradeoffs between feature resolution and build rate, for process and part selection and economic assessments will be discussed as DED is often traded against other AM processes. After highlighting typical DED design rules, we will also show how some of those rules can be broken via use cases and part designs that span the full spectrum of DED processes with examples of various DED part designs.

Additive Manufacturing↗

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.↗