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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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The mapping process discovers trends, needs, and capabilities from interviews with diverse groups of people, data analytics tools, and from publications. These are analyzed in the context of future scenarios to uncover problem areas that have the highest possible impact on the broadest number of people while ensuring that we are prepared for the future.
In an effort to expand routes for open communication and create additional opportunities for public involvement with NASA, Open Innovation Service Provider (OISP) methodologies have been incorporated as a tool in NASA's problem solving strategy. NASA engaged the services of two OISP providers, InnoCentive and Yet2.com, to test this novel approach and its feasibility in solving NASA s space flight challenges. The OISPs were chosen based on multiple factors including: network size and knowledge area span, established process, methodology, experience base, and cost. InnoCentive and Yet2.com each met the desired criteria; however each company s approach to Open Innovation is distinctly different. InnoCentive focuses on posting individual challenges to an established web-based network of approximately 200,000 solvers; viable solutions are sought and granted a financial award if found. Based on a specific technological need, Yet2.com acts as a talent scout providing a broad external network of experts as potential collaborators to NASA. A relationship can be established with these contacts to develop technologies and/or maintained as an established network of future collaborators. The results from the first phase of the pilot study have shown great promise for long term efficacy of utilizing the OISP methodologies. Solution proposals have been received for the challenges posted on InnoCentive and are currently under review for final disposition. In addition, Yet2.com has identified new external partners for NASA and we are in the process of understanding and acting upon these new opportunities. Compared to NASA's traditional routes for external problem solving, the OISP methodologies offered NASA a substantial savings in terms of time and resources invested. In addition, these strategies will help NASA extend beyond its current borders to build an ever expanding network of experts and global solvers.
The purpose here is to provide a top-level look at the stewardship functions performed in space operations, and to identify the major issues and challenges that must be addressed to build intelligent systems that can realistically support operations functions. The focus is on decision support activities involving monitoring, state assessment, goal generation, plan generation, and plan execution. The bottom line is that problem solving in the space operations domain is a very complex process. A variety of knowledge constructs, representations, and reasoning processes are necessary to support effective human problem solving. Emulating these kinds of capabilities in intelligent systems offers major technical challenges that the artificial intelligence community is only beginning to address.
This paper describes a new plugin architecture developed for the Copernicus spacecraft trajectory optimization program. Details of the software architecture design and development are described, as well as examples of how the capability can be used to extend the tool in order to expand the type of trajectory optimization problems that can be solved. The inclusion of plugins is a significant update to Copernicus, allowing user-created algorithms to be incorporated into the tool for the first time. The initial version of the new capability was released to the Copernicus user community with version 4.1 in March 2015, and additional refinements and improvements were included in the recent 4.2 release. It is proving quite useful, enabling Copernicus to solve problems that it was not able to solve before.
This paper describes a research-practice partnership involving twenty elementary teachers and a support team of university researchers and K-12 learning specialists. Over five years, the partnership explored ways to highlight and to expand computational thinking within math and science instruction. The project sought to increase student awareness, confidence, and fluency with computational thinking as a problem-solving approach, with particular attention to students from groups historically underrepresented in computer science. Based on feedback from across the partnership – together with analysis of student attitude and problem-solving data – this experience report identifies successes, challenges, and recommendations for future study.
Two project areas were pursued: the intelligent cockpit and human problem solving. The first area involves an investigation of the use of advanced software engineering methods to aid aircraft crews in procedure selection and execution. The second area is focused on human problem solving in dynamic environments, particulary in terms of identification of rule-based models land alternative approaches to training and aiding. Progress in each area is discussed.
Strategic foresight is used in the early stages of the CAS (Convergent Aeronautics Solutions) process. We discover trends, needs, and capabilities from interviews with diverse groups of people, data analytics tools, and from publications. These are analyzed in the context of future scenarios to uncover problem areas that have the highest possible impact on the broadest number of people while ensuring that we are prepared for the future.
Strategic foresight is used in the early stages of the CAS (Convergent Aeronautics Solutions) process. We discover trends, needs, and capabilities from interviews with diverse groups of people, data analytics tools, and from publications. These are analyzed in the context of future scenarios to uncover problem areas that have the highest possible impact on the broadest number of people while ensuring that we are prepared for the future.
Probe selection (PS) in machine diagnosis is viewed as a collection of models that apply under specific conditions. This makes it possible for three polynomial-time optimal algorithms to be developed for simplified PS models that allow different probes to have different costs. The work is compared with the research of Simon and Kadane (1975), who developed a collection of models for optimal problem-solving search. The relationship between these models and the three newly developed algorithms for PS is explored. Two of the algorithms are unlike the ones discussed by Simon and Kadane. The third cannot be related to the problem-solving models.
In the past year, the NASA s Office of Biological and Physical Research (OBPR) has formulated a long term plan to perform strategical and fundamental research bringing together physics, chemistry, biology, and engineering to solve problems needed for current and future agency mission goals. Materials Science is one of basic disciplines within the Enterprise s Division of Physical Sciences Research. The Materials Science Program participates to utilize effective use of International Space Station (ISS) and various world class ground laboratory facilities to solve new scientific and technology questions and transfer these results for public and agency benefits. The program has recently targeted new investigative research in strategic areas necessary to expand NASA knowledge base for exploration of the universe and some of these experiments will need access to the microgravity of space. The program is implementing a wide variety of traditional ground and flight based research related types of fundamental science related to materials crystallization, fundamental processing, and properties characterization in order to obtain basic understanding of various phenomena effects and relationships to the structures, processing, and properties of materials. , In addition new initiatives in radiation protection, materials for propulsion and In-space fabrication and repair focus on research helping the agency solve problems needed for future transportation into the solar system. A summary of the types and sources for this research is presented including those experiments planned for a low gravity environment. Areas to help expand the science basis for NASA future missions are described. An overview of the program is given including the scope of the current and future NASA Research Announcements with emphasis on new materials science initiatives. A description of the planned flight experiments to be conducted on the International Space Station program along with the planned facility class Materials Science Research Rack (MSRR) and Microgravity Glovebox (MSG) type investigations. Some initial results from the first three materials experiments are given.
Method which present a student with a more challenging and true to life situation of needing to conduct research in a problem solving context--and not thinking about organization of format until research and thinking are complete are investigated. Simulation-gaming techniques which attempt to teach initiative and creativity that library research are used for this purpose. However, it is shown case studies provide the greatest opportunities to engage the students in problem solving situations in which they develop skills as researchers and writers.
Blackboards are an Al problem solving methodology. A blackboard system consists of a structured data base (the blackboard) holding input and derived inferences and a collection of procedures for deriving inferences (knowledge sources). Each knowledge source is specialized to operate on some portion of the blackboard. The knowledge sources are invoked opportunistically as the information on the blackboard increases. The methodology is well suited to integrating multiple streams of fully reduced and qualitatively different data such as active radar track reports, passive electronic intelligence reports, and human intelligence reports about enemy intentions. This paper sketches the nature of the blackboard problem solving methodology with an emphasis on those features suiting it to such applications. The sketch is illustrated with examples from a relatively simple multi-system report integration problem. Relevant applications currently under development at Stanford's Knowledge Systems Laboratory are also described.
Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes.
MITRE Corporation's, A Review of Space Station Freedom Program Capabilities for the Development and Application of Advanced Automation, cites as a critical issue the following situation, extant at the NASA facilities visited in the course of preparing the review: The major issues noted with regard to design and research facilities deal with cooperative problem solving, technology transfer, and communication between these facilities. While the authors were visiting lab and test beds to collect information, personnel at many of these facilities were interested in any information they could collect on activities at other facilities. A formal means of gathering this information could not be identified by these personnel. While communication between some facilities was taking place or was planned, for technology transfer or coordination of schedules (e.g., for SADP demonstrations), poor communication between these facilities could lead to a lack of technical standards, duplication of effort, poorly defined interfaces, scheduling problems, and increased cost. Formal mechanisms by which effective communication and cooperative problem solving can take place, and information can be disseminated, must be defined. A solution is proposed for the communications aspects of the issues addressed above; and offered at the same time a solution which can prove effective in dealing with some of the problems being encountered with expertise being lost via retirement or defection to the private sector. The proffered recommendations are recognizably cost-effective and tap the rising sector of expert knowledge being produced by the American academic community.
Much of school learning consists of rote memorization of facts with little emphasis on meaningful interpretations. Knowledge construction is reduced to factual knowledge production with little regard for critical thinking, problem solving, or clarifying misconceptions. An important role of a middle and secondary teacher when teaching science is to aid students' ability to reflect upon what they know about a given topic and make available strategies that will enhance their understanding of text and science experiments. Developing metacognition, the ability to monitor one's own knowledge about a topic of study and to activate appropriate strategies, enhances students' learning when faced with reading, writing and problem solving situations. Two instructional strategies that can involve students in developing metacognitive awareness are hierarchical concept mapping, and Vee diagrams. Concept maps enable students to organize their ideas and reveal visually these ideas to others. A Vee diagram is a structured visual means of relating the methodological aspects of an activity to its underlying conceptual aspect in ways that aid learners in meaningful understanding of scientific investigations.
An investigation was made of interaction between a human pilot and automated on-board decision making systems. Research was initiated on the topic of pilot problem solving in automated and semi-automated flight management systems and attempts were made to develop a model of human decision making in a multi-task situation. A study was made of allocation of responsibility between human and computer, and discussed were various pilot performance parameters with varying degrees of automation. Optimal allocation of responsibility between human and computer was considered and some theoretical results found in the literature were presented. The pilot as a problem solver was discussed. Finally the design of displays, controls, procedures, and computer aids for problem solving tasks in automated and semi-automated systems was considered.
Work focused on two areas in machine learning: representation for inductive learning and how to apply concept learning techniques to learning state preferences, which can represent search control knowledge for problem solving. Specifically, in the first area the issues of the effect of representation on learning, on how learning formalisms are biased, and how concept learning can benefit from the use of a hybrid formalism are addressed. In the second area, the issues of developing an agent to learn search control knowledge from the relative values of states, of the source of that qualitative information, and of the ability to use both quantitative and qualitative information in order to develop an effective problem-solving policy are examined.
The goal of the Autonomous Power System program is to develop and apply intelligent problem solving and control to the Space Station Freedom's electric power testbed being developed at NASA's Lewis Research Center. Objectives are to establish artificial intelligence technology paths, craft knowledge-based tools and products for power systems, and integrate knowledge-based and conventional controllers. This program represents a joint effort between the Space Station and Office of Aeronautics and Space Technology to develop and demonstrate space electric power automation technology capable of: (1) detection and classification of system operating status, (2) diagnosis of failure causes, and (3) cooperative problem solving for power scheduling and failure recovery. Program details, status, and plans will be presented.