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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Artificial intelligence techniques for scheduling Space Shuttle missions

Planning and scheduling of NASA Space Shuttle missions is a complex, labor-intensive process requiring the expertise of experienced mission planners. We have developed a planning and scheduling system using combinations of artificial intelligence knowledge representations and planning techniques to capture mission planning knowledge and automate the multi-mission planning process. Our integrated object oriented and rule-based approach reduces planning time by orders of magnitude and provides planners with the flexibility to easily modify planning knowledge and constraints without requiring programming expertise.

Henke, Andrea L.↗
NASA NTRS2003

The Power of a Question: A Case Study of Two Organizational Knowledge Capture Systems

This document represents a presentation regarding organizational knowledge capture systems which was delivered at the HICSS-36 conference held from January 6-9, 2003. An exploratory case study of two knowledge resources is offered. Then, two organizational knowledge capture systems are briefly described: knowledge transfer from practitioner and the use of questions to represent knowledge. Finally, the creation of a database of peer review questions is suggested as a method of promoting organizational discussions and knowledge representation and exchange.

case study↗
NASA NTRS1989

Hubble Space Telescope Design Engineering Knowledgebase (HSTDEK)

The research covered here pays specific attention to the development of tools to assist knowledge engineers in acquiring knowledge and to assist other technical, engineering, and management personnel in automatically performing knowledge capture as part of their everyday work without adding any extra work to what they already do. Requirements for data products, the knowledge base, and methods for mapping knowledge in the documents onto the knowledge representations are discussed, as are some of the difficulties of capturing in the knowledge base the structure of the design process itself, along with a model of the system designed. The capture of knowledge describing the interactions of different components is also discussed briefly.

Johannes, James D.↗
NASA NTRS1985

Reasoning about procedural knowledge

A crucial aspect of automated reasoning about space operations is that knowledge of the problem domain is often procedural in nature - that is, the knowledge is often in the form of sequences of actions or procedures for achieving given goals or reacting to certain situations. In this paper a system is described that explicitly represents and reasons about procedural knowledge. The knowledge representation used is sufficiently rich to describe the effects of arbitrary sequences of tests and actions, and the inference mechanism provides a means for directly using this knowledge to reach desired operational goals. Furthermore, the representation has a declarative semantics that provides for incremental changes to the system, rich explanatory capabilities, and verifiability. The approach also provides a mechanism for reasoning about the use of this knowledge, thus enabling the system to choose effectively between alternative courses of action.

Georgeff, M. P.↗
NASA NTRS1989

Advanced Software Development Workstation Project

The Advanced Software Development Workstation Project, funded by Johnson Space Center, is investigating knowledge-based techniques for software reuse in NASA software development projects. Two prototypes have been demonstrated and a third is now in development. The approach is to build a foundation that provides passive reuse support, add a layer that uses domain-independent programming knowledge, add a layer that supports the acquisition of domain-specific programming knowledge to provide active support, and enhance maintainability and modifiability through an object-oriented approach. The development of new application software would use specification-by-reformulation, based on a cognitive theory of retrieval from very long-term memory in humans, and using an Ada code library and an object base. Current tasks include enhancements to the knowledge representation of Ada packages and abstract data types, extensions to support Ada package instantiation knowledge acquisition, integration with Ada compilers and relational databases, enhancements to the graphical user interface, and demonstration of the system with a NASA contractor-developed trajectory simulation package. Future work will focus on investigating issues involving scale-up and integration.

Lee, Daniel↗
NASA NTRS1992

An expert system for integrated structural analysis and design optimization for aerospace structures

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.

Source record↗
NASA NTRS1988

Toward a computational theory for motion understanding: The expert animators model

Artificial intelligence researchers claim to understand some aspect of human intelligence when their model is able to emulate it. In the context of computer graphics, the ability to go from motion representation to convincing animation should accordingly be treated not simply as a trick for computer graphics programmers but as important epistemological and methodological goal. In this paper we investigate a unifying model for animating a group of articulated bodies such as humans and robots in a three-dimensional environment. The proposed model is considered in the framework of knowledge representation and processing, with special reference to motion knowledge. The model is meant to help setting the basis for a computational theory for motion understanding applied to articulated bodies.

Mohamed, Ahmed S.↗
NASA NTRS1992

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning.

Lowry, Michael R.↗
NASA NTRS2003

The Digital Space Shuttle, 3D Graphics, and Knowledge Management

The Digital Shuttle is a knowledge management project that seeks to define symbiotic relationships between 3D graphics and formal knowledge representations (ontologies). 3D graphics provides geometric and visual content, in 2D and 3D CAD forms, and the capability to display systems knowledge. Because the data is so heterogeneous, and the interrelated data structures are complex, 3D graphics combined with ontologies provides mechanisms for navigating the data and visualizing relationships.

Gomez, Julian E.↗
NASA NTRS1997

An Arbitrary First Order Theory Can Be Represented by a Program: A Theorem

How can we represent knowledge inside a computer? For formalized knowledge, classical logic seems to be the most adequate tool. Classical logic is behind all formalisms of classical mathematics, and behind many formalisms used in Artificial Intelligence. There is only one serious problem with classical logic: due to the famous Godel's theorem, classical logic is algorithmically undecidable; as a result, when the knowledge is represented in the form of logical statements, it is very difficult to check whether, based on this statement, a given query is true or not. To make knowledge representations more algorithmic, a special field of logic programming was invented. An important portion of logic programming is algorithmically decidable. To cover knowledge that cannot be represented in this portion, several extensions of the decidable fragments have been proposed. In the spirit of logic programming, these extensions are usually introduced in such a way that even if a general algorithm is not available, good heuristic methods exist. It is important to check whether the already proposed extensions are sufficient, or further extensions is necessary. In the present paper, we show that one particular extension, namely, logic programming with classical negation, introduced by M. Gelfond and V. Lifschitz, can represent (in some reasonable sense) an arbitrary first order logical theory.

Hosheleva, Olga↗
NASA NTRS1990

Methodology for automating software systems

Applying ITS technology to the shuttle diagnostics would not require the rigor of the Petri Net representation, however it is important in providing the animated simulated portion of the interface and the demands placed on the system to support the training aspects to have a homogeneous and consistent underlying knowledge representation. By keeping the diagnostic rule base, the hardware description, the software description, user profiles, desired behavioral knowledge, and the user interface in the same notation, it is possible to reason about the all of the properties of petri nets, on any selected portion of the simulation. This reasoning provides foundation for utilization of intelligent tutoring systems technology.

Moseley, Warren↗
NASA NTRS1995

Applying AI tools to operational space environmental analysis

The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines events covering reports of natural phenomena such as solar flares, bursts, geomagnetic storms, and five others pertinent to space environmental analysis. With our preliminary event definitions we experimented with TAS's support for temporal pattern analysis using X-ray flare and geomagnetic storm forecasts as case studies. We are currently working on a framework for integrating advanced graphics and space environmental models into this analytical environment.

Krajnak, Mike↗
NASA NTRS1992

Toward domain-specific design environments: Some representation ideas from the telecommunications domain

ACME is an experimental environment for investigating new approaches to modeling and analysis of system requirements and designs. ACME is built on and extends object-oriented conceptual modeling techniques and knowledge representation and reasoning (KRR) tools. The most immediate intended use for ACME is to help represent, understand, and communicate system designs during the early stages of system planning and requirements engineering. While our research is ostensibly aimed at software systems in general, we are particularly motivated to make an impact in the telecommunications domain, especially in the area referred to as Intelligent Networks (IN's). IN systems contain the software to provide services to users of a telecommunications network (e.g., call processing services, information services, etc.) as well as the software that provides the internal infrastructure for providing the services (e.g., resource management, billing, etc.). The software includes not only systems developed by the network proprietors but also by a growing group of independent service software providers.

Greenspan, Sol↗
NASA NTRS2004

Using Ontologies to Formalize Services Specifications in Multi-Agent Systems

One key issue in multi-agent systems (MAS) is their ability to interact and exchange information autonomously across applications. To secure agent interoperability, designers must rely on a communication protocol that allows software agents to exchange meaningful information. In this paper we propose using ontologies as such communication protocol. Ontologies capture the semantics of the operations and services provided by agents, allowing interoperability and information exchange in a MAS. Ontologies are a formal, machine processable, representation that allows to capture the semantics of a domain and, to derive meaningful information by way of logical inference. In our proposal we use a formal knowledge representation language (OWL) that translates into Description Logics (a subset of first order logic), thus eliminating ambiguities and providing a solid base for machine based inference. The main contribution of this approach is to make the requirements explicit, centralize the specification in a single document (the ontology itself), at the same that it provides a formal, unambiguous representation that can be processed by automated inference machines.

Breitman, Karin Koogan↗
NASA NTRS1992

GATOR: Requirements capturing of telephony features

We are developing a natural language-based, requirements gathering system called GATOR (for the GATherer Of Requirements). GATOR assists in the development of more accurate and complete specifications of new telephony features. GATOR interacts with a feature designer who describes a new feature, set of features, or capability to be implemented. The system aids this individual in the specification process by asking for clarifications when potential ambiguities are present, by identifying potential conflicts with other existing features, and by presenting its understanding of the feature to the designer. Through user interaction with a model of the existing telephony feature set, GATOR constructs a formal representation of the new, 'to be implemented' feature. Ultimately GATOR will produce a requirements document and will maintain an internal representation of this feature to aid in future design and specification. This paper consists of three sections that describe (1) the structure of GATOR, (2) POND, GATOR's internal knowledge representation language, and (3) current research issues.

Dankel, Douglas D., II↗
NASA NTRS1986

Development of a knowledge acquisition tool for an expert system flight status monitor

Two of the main issues in artificial intelligence today are knowledge acquisition dion and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. The knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use is discussed. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

Disbrow, J. D.↗
NASA NTRS1986

Development of a knowledge acquisition tool for an expert system flight status monitor

Two of the main issues in artificial intelligence today are knowledge acquisition and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. This paper discusses the knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

Disbrow, J. D.↗
NASA NTRS2000

AI's Philosophical Underpinnings: A Thinking Person's Walk through the Twists and Turns of Artificial Intelligence's Meandering Path

Few human endeavors can be viewed both as extremely successful and unsuccessful at the same time. This is typically the case when goals have not been well defined or have been shifting in time. This has certainly been true of Artificial Intelligence (AI). The nature of intelligence has been the object of much thought and speculation throughout the history of philosophy. It is in the nature of philosophy that real headway is sometimes made only when appropriate tools become available. Similarly the computer, coupled with the ability to program (at least in principle) any function, appeared to be the tool that could tackle the notion of intelligence. To suit the tool, the problem of the nature of intelligence was soon sidestepped in favor of this notion: If a probing conversation with a computer could not be distinguished from a conversation with a human, then AI had been achieved. This notion became known as the Turing test, after the mathematician Alan Turing who proposed it in 1950. Conceptually rich and interesting, these early efforts gave rise to a large portion of the field's framework. Key to AI, rather than the 'number crunching' typical of computers until then, was viewed as the ability to manipulate symbols and make logical inferences. To facilitate these tasks, AI languages such as LISP and Prolog were invented and used widely in the field. One idea that emerged and enabled some success with real world problems was the notion that 'most intelligence' really resided in knowledge. A phrase attributed to Feigenbaum, one of the pioneers, was 'knowledge is the power.' With this premise, the problem is shifted from 'how do we solve problems' to 'how do we represent knowledge.' A good knowledge representation scheme could allow one to draw conclusions from given premises. Such schemes took forms such as rules,frames and scripts. It allowed the building of what became known as expert systems or knowledge based systems (KBS).

Colombano, Silvano↗