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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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One important approach to enhancing software re-use is through the creation of large-scale software libraries.
Automated planning systems (APS) are gaining acceptance for use on NASA missions as evidenced by APS flown On missions such as Orbiter and Deep Space 1 both of which were commanded by onboard planning systems. The planning system takes high level goals and expands them onboard into a detailed of action fiat the spacecraft executes. The system must be verified to ensure that the automatically generated plans achieve the goals as expected and do not generate actions that would harm the spacecraft or mission. These systems are typically tested using empirical methods. Formal methods, such as model checking, offer exhaustive or measurable test coverage which leads to much greater confidence in correctness. This paper describes a formal method based on the SPIN model checker. This method guarantees that possible plans meet certain desirable properties. We express the input model in Promela, the language of SPIN and express the properties of desirable plans formally.
Due to the complexity of the Space Station Electrical Power System (EPS) as currently envisioned, artificial intelligence/expert system techniques are being investigated to automate operations, maintenance, and diagnostic functions. A study was conducted to investigate this technology as it applies to failure detection, isolation, and reconfiguration (FDIR) and health monitoring of power system components and of the total system. Control system utilization of expert systems for load scheduling and shedding operations was also researched. A discussion of the utilization of artificial intelligence/expert systems for Initial Operating Capability (IOC) for the Space Station effort is presented along with future plans at Rocketdyne for the utilization of this technology for enhanced Space Station power capability.
This is a presentation for the Interagency Advanced Power Group (IAPG) Electrical Systems Working Group (ESWG) Artificial Intelligence/Machine Learning Panel (AI/ML).
Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling large arrays of control devices on innovative air vehicles. Artificial neutral networks are inspired by biological nervous systems and neurocomputing has successfully been applied to a variety of complex optimization problems. This project investigates the potential of applying neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while keeping system weights within acceptable limits. The main objective of this project is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces. A Nastran aeroservoelastic finite element model is used to generate a learning database of hinge moment and actuation power characteristics for an array of flight conditions and control surface deflections. An artificial neural network incorporating a genetic algorithm then uses this training data to perform control allocation optimization for the investigated aircraft configuration. The phase I project showed that optimization results for the sum of required hinge moments are improved by more than 12% over the best Nastran solution by using the neural network optimization process.
In this paper, we will present a look at the current state of the art in human-computer interface technologies, including intelligent interactive agents, natural speech interaction and gestural based interfaces. We describe our use of these technologies to implement a cost effective, immersive experience on a public region in Second Life. We provision our Artificial Agents as a German Shepherd Dog avatar with an external rules engine controlling the behavior and movement. To interact with the avatar, we implemented a natural language and gesture system allowing the human avatars to use speech and physical gestures rather than interacting via a keyboard and mouse. The result is a system that allows multiple humans to interact naturally with AI avatars by playing games such as fetch with a flying disk and even practicing obedience exercises using voice and gesture, a natural seeming day in the park.
In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.
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DENDRAL program used to construct all possible acyclic structural isomers of C, H, N and O
Computer program to decide structure for aliphatic ketone from low resolution mass spectrum using DENDRAL algorithm
Aliphatic ethers low resolution mass spectra interpretation using computer program heuristic DENDRAL
Computerized method of interpreting low resolution mass spectra in organic chemical analysis, describing inference maker program applications and results
Discussion of the modification of the DENDRAL computer program to extend the program to cyclic structures which exceed numerically the linear molecules of a given composition. IR, NMR and mass spectroscopy is used to develop a method for identification of each of the 27 possible ketones (exclusive of 5 cyclopropanones) of composition C6H10O.
Both in practical engineering and in control of muscular systems, low level subsystems automatically provide crude approximations to the proper response. Through low level tuning of these approximations, the proper response variant can emerge from standardized high level commands. Such systems are expressly suited to emerging large scale integrated circuit technology. A computer, using symbolic descriptions of subsystem responses, can select and shape responses of low level digital or analog microcircuits. A mathematical theory that reveals significant informational units in this style of control and software for realizing such information structures are formulated.
The need for remotely controlled manipulators and explorers, coupled with emerging large-scale integrated (LSI) circuit technology, foreshadow the dominant role of a style of hybrid control. Systems having many degrees of freedom should be guided and regulated by controllers to which commands are given in purpose-oriented form, to be converted into appropriate machine-oriented signals to effectors for achieving the purpose. In particular, numerical tabulations of functions should be replaced by low-level analog microcircuit realizations of the functions, particularly when feedback can keep errors from piling up. The study is concerned with new problems and organizations that arise in attempting to exploit obvious principles to the fullest degree.
Introduction of a language for problem solving and specifically robot planning, program verification, and synthesis and theorem proving. This language, called question-answerer 4 (QA4), embodies many features that have been found useful for constructing problem solvers but have to be programmed explicitly by the user of a conventional language. The most important features of QA4 are described, and examples are provided for most of the material introduced. Language features include backtracking, parallel processing, pattern matching, set manipulation, and pattern-triggered function activation. The language is most convenient for use in an interactive way and has extensive trace and edit facilities.
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