690,197 research outputs found

    Considerations in development of expert systems for real-time space applications

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    Over the years, demand on space systems has increased tremendously and this trend will continue for the near future. Enhanced capabilities of space systems, however, can only be met with increased complexity and sophistication of onboard and ground systems. Artificial Intelligence and expert system techniques have great potential in space applications. Expert systems could facilitate autonomous decision making, improve in-orbit fault diagnosis and repair, enhance performance and reduce reliance on ground support. However, real-time expert systems, unlike conventional off-line consultative systems, have to satisfy certain special stringent requirements before they could be used for onboard space applications. Challenging and interesting new environments are faced while developing expert system space applications. This paper discusses the special characteristics, requirements and typical life cycle issues for onboard expert systems. Further, it also describes considerations in design, development, and implementation which are particularly important to real-time expert systems for space applications

    Knowledge representation by connection matrices: A method for the on-board implementation of large expert systems

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    Extremely large knowledge sources and efficient knowledge access characterizing future real-life artificial intelligence applications represent crucial requirements for on-board artificial intelligence systems due to obvious computer time and storage constraints on spacecraft. A type of knowledge representation and corresponding reasoning mechanism is proposed which is particularly suited for the efficient processing of such large knowledge bases in expert systems

    Considerations in development of expert systems for real-time space applications

    Get PDF
    Over the years demand on space systems have been increased tremendously and this trend will continue for the near future. The enhanced capabilities of space systems, however, can only be met with increased complexity and sophistication of onboard and ground systems, and artificial intelligence and expert system concepts have a significant role in space applications. Expert systems could facilitate decision making, improved fault diagnosis and repair, enhanced performance and less reliance on ground support. However, some requirements have to be fulfilled before practical use of flight-worthy expert systems for onboard (and ground) operations. This paper discusses some of the characteristics and important considerations in design, development, implementation and use of expert systems for real-life space applications. Further, it describes a typical life cycle of expert system development and its usage

    Requirements-driven Social Adaptation: Expert Survey

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    Self-adaptation empowers systems with the capability to meet stakeholders’ requirements in a dynamic environment. Such systems autonomously monitor changes and events which drive adaptation decisions at runtime. Social Adaptation is a recent kind of requirements-driven adaptation which enables users to give a runtime feedback on the success and quality of a system’s configurations in reaching their requirements. The system analyses users’ feedback, infers their collective judgement and then uses it to shape its adaptation decisions. [Question/problem] However, there is still a lack of engineering mechanisms to guarantee a correct conduction of Social Adapta- tion. [Principal ideas/results] In this paper, we conduct a two-phase Expert Sur- vey to identify core benefits, domain areas and challenges for Social Adaptation. [Contribution] Our findings provide practitioners and researchers in adaptive systems engineering with insights on this emerging role of users, or the crowd, and stimulate future research to solve the open problems in this area

    Software Analyzes Complex Systems in Real Time

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    Expert system software programs, also known as knowledge-based systems, are computer programs that emulate the knowledge and analytical skills of one or more human experts, related to a specific subject. SHINE (Spacecraft Health Inference Engine) is one such program, a software inference engine (expert system) designed by NASA for the purpose of monitoring, analyzing, and diagnosing both real-time and non-real-time systems. It was developed to meet many of the Agency s demanding and rigorous artificial intelligence goals for current and future needs. NASA developed the sophisticated and reusable software based on the experience and requirements of its Jet Propulsion Laboratory s (JPL) Artificial Intelligence Research Group in developing expert systems for space flight operations specifically, the diagnosis of spacecraft health. It was designed to be efficient enough to operate in demanding real time and in limited hardware environments, and to be utilized by non-expert systems applications written in conventional programming languages. The technology is currently used in several ongoing NASA applications, including the Mars Exploration Rovers and the Spacecraft Health Automatic Reasoning Pilot (SHARP) program for the diagnosis of telecommunication anomalies during the Neptune Voyager Encounter. It is also finding applications outside of the Space Agency

    Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI

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    Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission’s AI high-level expert group and established procedures from software engineering and raise questions for future work.publishedVersio

    LadderBot: A requirements self-elicitation system

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    Digital transformation impacts an ever-increasing amount of everyone’s business and private life. It is imperative to incorporate user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by users that are novices at contributing requirements to IS development projects. [Objective] We need to develop RE systems that are capable of assisting a wide audience of users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. [Research Method] We present the prototypical self-elicitation system “LadderBot”. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human (expert) interviewer’s capability to rephrase questions and provide assistance in the process. An experimental study is proposed to evaluate LadderBot against an established questionnaire-based laddering approach. [Contribution] This work-in-progress introduces the chatbot LadderBot as a tool to guide novice users during requirements self-elicitation using the laddering technique. Furthermore, we present the design of an experimental study and outline the next steps and a vision for the future

    A Delphi-Based Framework for systems architecting of in-orbit exploration infrastructure for human exploration beyond Low Earth Orbit

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    The current debate in the U.S. Human Spaceflight Program focuses on the development of the next generation of man-rated heavy lift launch vehicles. While launch vehicle systems are of critical importance for future exploration, a comprehensive analysis of the entire exploration infrastructure is required to avoid costly pitfalls at early stages of the design process. This paper addresses this need by presenting a Delphi-Based Systems Architecting Framework for integrated architectural analysis of future in-orbit infrastructure for human space exploration beyond Low Earth Orbit. The paper is structured in two parts. The first part consists of an expert elicitation study to identify objectives for the in-space transportation infrastructure. The study was conducted between November 2011 and January 2012 with 15 senior experts involved in human spaceflight in the United States and Europe. The elicitation study included the formation of three expert panels representing exploration, science, and policy stakeholders engaged in a 3-round Delphi study. The rationale behind the Delphi approach, as imported from social science research, is discussed. Finally, a novel version of the Delphi method is presented and applied to technical decision-making and systems architecting in the context of human space exploration. The second part of the paper describes a tradespace exploration study of in-orbit infrastructure coupled with a requirements definition exercise informed by expert elicitation. The uncertainties associated with technical requirements and stakeholder goals are explicitly considered in the analysis. The outcome of the expert elicitation process portrays an integrated view of perceived stakeholder needs within the human spaceflight community. Needs are subsequently converted into requirements and coupled to the system architectures of interest to analyze the correlation between exploration, science, and policy goals. Pareto analysis is used to identify architectures of interest for further consideration by decision-makers. The paper closes with a summary of insights and develops a strategy for evolutionary development of the exploration infrastructure of the incoming decades. The most important result produced by this analysis is the identification of a critical irreducible ambiguity undermining value delivery for the in-space transportation infrastructure of the next three decades: destination choice. Consensus on destination is far from being reached by the community at large, with particular reference to exploration and policy stakeholders. The realization of this ambiguity is a call for NASA to promote an open forum on this topic, and to develop a strong case for policy makers to incentivize investments in the human spaceflight industry in the next decades

    Supporting the Development of Cyber-Physical Systems with Natural Language Processing: A Report

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    Software has become the driving force for innovations in any technical system that observes the environment with different sensors and influence it by controlling a number of actuators; nowadays called Cyber-Physical System (CPS). The development of such systems is inherently inter-disciplinary and often contains a number of independent subsystems. Due to this diversity, the majority of development information is expressed in natural language artifacts of all kinds. In this paper, we report on recent results that our group has developed to support engineers of CPSs in working with the large amount of information expressed in natural language. We cover the topics of automatic knowledge extraction, expert systems, and automatic requirements classification. Furthermore, we envision that natural language processing will be a key component to connect requirements with simulation models and to explain tool-based decisions. We see both areas as promising for supporting engineers of CPSs in the future
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