Application of flight systems methodologies to the validation of knowledge-based systems

Flight and mission-critical systems are verified, qualified for flight, and validated using well-known and well-established techniques. These techniques define the validation methodology used for such systems. In order to verify, qualify, and validate knowledge-based systems (KBS's), the methodology used for conventional systems must be addressed, and the applicability and limitations of that methodology to KBS's must be identified. The author presents an outline of how this approach to the validation of KBS's is being developed and used at the Dryden Flight Research Facility of the NASA Ames Research Center

A NASA/RAE cooperation in the development of a real-time knowledge-based autopilot

As part of a US/UK cooperative aeronautical research program, a joint activity between the NASA Dryden Flight Research Facility and the Royal Aerospace Establishment on knowledge-based systems was established. This joint activity is concerned with tools and techniques for the implementation and validation of real-time knowledge-based systems. The proposed next stage of this research is described, in which some of the problems of implementing and validating a knowledge-based autopilot for a generic high-performance aircraft are investigated

Semi-automatic conceptual data modeling using entity and relationship instance repositories

Conceptual modeling is the foundation of analysis and design methodologies for the development of information systems. It is challenging because it requires a clear understanding of an application domain and an ability to translate the requirement specifications into a data model. However, novice designers frequently lack experience and have incomplete knowledge about the application being designed. We propose new types of reusable artifacts called Entity Instance Repository (EIR) and Relationship Instance Repository (RIR), which contain ER (Entity-Relationship) modeling patterns from prior designs and serve as knowledge-based repositories for conceptual modeling. We also select six data modeling rules to check whether they are comprehensive enough in creating quality conceptual models. This research aims to develop effective knowledge-based systems (KBSs) with EIR and RIR. Our proposed artifacts are likely to be useful for conceptual designs in the following aspects: (1) they contain knowledge about a domain; (2) automatic generation of EIR and RIR overcomes a major problem ofinefficient manual approaches that depend on experienced modeling designers and domain experts; and (3) they are domain-specific and therefore easier to understand and reuse. Two KBSs were developed in this study: Heuristic-Based Technique (HBT) and Entity Instance Pattern WordNet (EIPW). The goals of this study are (1) to find effective approaches that can improve the novice designers’ performance in developing conceptual models by integrating pattern-based technique and various modeling techniques, (2) to evaluate whether those selected six modeling rules are effective in HBT, and (3) to validate whether the proposed KBSs are effective in creating quality conceptual models. To assess the potential of the KBSs to benefit practice, empirical testing was conductedon tasks of different sizes. The empirical results indicate that novice designers’ overall performance increased by 30.9~46.0 % when using EIPW, and increased by 33.5~34.9% when using HBT, compared with the cases of no tools.Ph.D., Information Studies -- Drexel University, 201

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report

Web application for reliability analysis within civil aviation domain

Analýzy spolehlivosti jsou klíčovými složkami při hodnocení posouzení rizik během fáze návrhu v leteckém průmyslu. Analýza stromu poruch (FTA) a analýza poruchových režimů a efektů (FMEA) se běžně kombinují při analýze systému a vyhodnocování možných poruch. Kombinování metodik vyžaduje sjednocení struktury dat tak, aby byla použitelná pro všechny analytické metody zároveň. Existující aplikace poskytují nástroje samostatně, což vede k nekonzistenci dat, duplikátům a překlepům při migraci napříč aplikacemi. Tato práce si klade za cíl vytvořit rozšiřitelné řešení, které by poskytlo nástroje k provedení jedné z technik FTA a FMEA a přitom se spoléhalo na ontologický model použitelný pro obě techniky zároveň. Diplomová práce analyzuje existující řešení a ontologie a na základě těchto vstupů navrhuje nezbytné požadavky, které jsou ve spolupráci se zúčastněnými doménovými odborníky prioritizovány. Výsledné řešení implementuje aplikaci zaměřenou primárně na FTA, která nabízí definování partonomie systému, konstrukci FTA a automatický převod stromů do FMEA vzhledem k jednotnému ontologickému modelu. Aplikace je na závěr otestována doménovými odborníky na základě skutečných leteckých dat.Reliability analyses are key components in a risk assessment evaluation during the design phase in an aviation industry. Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA) are commonly combined together to review the system and to evaluate possible failures. The combination of methodologies requires a unified data usable for all the analyses. Existing applications provide the tools separately which introduces inconsistencies, duplicates and typos when the data are migrated across the applications. This thesis thus aims to create an extensible solution that would provide tools to perform one of FTA and FMEA techniques and yet rely on an ontological model usable for both. The thesis analyses existing solutions and ontologies and given these inputs proposes necessary requirements that are prioritized in cooperation with involved domain experts. The resulting solution implements an application focusing primarily on FTA which offers possibilities for system partonomy definition, FTA construction and an automatic conversion of the trees to FMEA tables given the unified ontological model. The application is finally reviewed by the domain experts on real aviation data

A framework for managing global risk factors affecting construction cost performance

Poor cost performance of construction projects has been a major concern for both contractors and clients. The effective management of risk is thus critical to the success of any construction project and the importance of risk management has grown as projects have become more complex and competition has increased. Contractors have traditionally used financial mark-ups to cover the risk associated with construction projects but as competition increases and margins have become tighter they can no longer rely on this strategy and must improve their ability to manage risk. Furthermore, the construction industry has witnessed significant changes particularly in procurement methods with clients allocating greater risks to contractors. Evidence shows that there is a gap between existing risk management techniques and tools, mainly built on normative statistical decision theory, and their practical application by construction contractors. The main reason behind the lack of use is that risk decision making within construction organisations is heavily based upon experience, intuition and judgement and not on mathematical models. This thesis presents a model for managing global risk factors affecting construction cost performance of construction projects. The model has been developed using behavioural decision approach, fuzzy logic technology, and Artificial Intelligence technology. The methodology adopted to conduct the research involved a thorough literature survey on risk management, informal and formal discussions with construction practitioners to assess the extent of the problem, a questionnaire survey to evaluate the importance of global risk factors and, finally, repertory grid interviews aimed at eliciting relevant knowledge. There are several approaches to categorising risks permeating construction projects. This research groups risks into three main categories, namely organisation-specific, global and Acts of God. It focuses on global risk factors because they are ill-defined, less understood by contractors and difficult to model, assess and manage although they have huge impact on cost performance. Generally, contractors, especially in developing countries, have insufficient experience and knowledge to manage them effectively. The research identified the following groups of global risk factors as having significant impact on cost performance: estimator related, project related, fraudulent practices related, competition related, construction related, economy related and political related factors. The model was tested for validity through a panel of validators (experts) and crosssectional cases studies, and the general conclusion was that it could provide valuable assistance in the management of global risk factors since it is effective, efficient, flexible and user-friendly. The findings stress the need to depart from traditional approaches and to explore new directions in order to equip contractors with effective risk management tools

A model for a knowledge-based system's life cycle

The American Institute of Aeronautics and Astronautics has initiated a Committee on Standards for Artificial Intelligence. Presented here are the initial efforts of one of the working groups of that committee. The purpose here is to present a candidate model for the development life cycle of Knowledge Based Systems (KBS). The intent is for the model to be used by the Aerospace Community and eventually be evolved into a standard. The model is rooted in the evolutionary model, borrows from the spiral model, and is embedded in the standard Waterfall model for software development. Its intent is to satisfy the development of both stand-alone and embedded KBSs. The phases of the life cycle are detailed as are and the review points that constitute the key milestones throughout the development process. The applicability and strengths of the model are discussed along with areas needing further development and refinement by the aerospace community