Knowledge-Based System for Flight Information Management

The use of knowledge-based system (KBS) architectures to manage information on the primary flight display (PFD) of commercial aircraft is described. The PFD information management strategy used tailored the information on the PFD to the tasks the pilot performed. The KBS design and implementation of the task-tailored PFD information management application is described. The knowledge acquisition and subsequent system design of a flight-phase-detection KBS is also described. The flight-phase output of this KBS was used as input to the task-tailored PFD information management KBS. The implementation and integration of this KBS with existing aircraft systems and the other KBS is described. The flight tests are examined of both KBS's, collectively called the Task-Tailored Flight Information Manager (TTFIM), which verified their implementation and integration, and validated the software engineering advantages of the KBS approach in an operational environment

The role of computational logic as a hinge paradigm among deduction, problem solving, programming, and parallelism

This paper presents some brief considerations on the role of Computational Logic in the construction of Artificial Intelligence systems and in programming in general. It does not address how the many problems in AI can be solved but, rather more modestly, tries to point out some advantages of Computational Logic as a tool for the AI scientist in his quest. It addresses the interaction between declarative and procedural views of programs (deduction and action), the impact of the intrinsic limitations of logic, the relationship with other apparently competing computational paradigms, and finally discusses implementation-related issues, such as the efficiency of current implementations and their capability for efficiently exploiting existing and future sequential and parallel hardware. The purpose of the discussion is in no way to present Computational Logic as the unique overall vehicle for the development of intelligent systems (in the firm belief that such a panacea is yet to be found) but rather to stress its strengths in providing reasonable solutions to several aspects of the task

Software construction by composition of components

In the continuously evolving era of software technological advances, software complexity and requirements change grow at increasing paces. Developing software using traditional approaches to meet the growing demand for functionalities and computation, in particular for large scale software, produces software applications which are characterized as being monolithic, difficult to reuse and costly to develop. To address those issues, componentbased software development has emerged among other approaches and has already produced a noteworthy positive impact. Nevertheless, component-based software development still suffers from a number of drawbacks and limitations. The aim of this research project is to improve software construction by composition of components. Traditionally, reusable components may exhibit a bloating syndrome caused by bundled but unused set of members which vary according to application contexts and business domains. Furthermore, reusable components may suffer from chaotic amalgamation of code elements. Component members' bloating and a chaotic amalgamation of code elements limitations can be partially attributed to the lack of modular components. Typically, software constmctors rewrite from scratch newer software components (even though many code parts exist), retrofit or customize existing components to satisfy applications' requirements. In addition to these two limitations, software components suffer also from version mismatches due to the use of different versions of the same component. To address the aforementioned limitations a new approach is proposed in this research work. Our approach is based on composition of atomic or enhanced modularity components. This new approach will contribute to the improvement of component-based software construction by alleviating some of the limitations facing it. It The focus of our research targets particularly the constmction phase of the software development lifecycle. The main generic goal of this research project is: • To improve software construction based on components composition by providing an approach which shifts and promotes software construction from a traditional construction approach based heavily on code writing and amalgamation to an approach relying increasingly on components composition. The specific objectives of this research are: • To propose and specify a software component model which provides remedies for some of the limitations facing software construction by components composition. • To provide a reference implementation for this component model. • To design a measurement method to measure components" unwanted members. • To provide a prototype tool to measure components" unwanted members. • To propose a component versioning mechanism. • To provide a prototype tool to detect component versions mismatches. The approach presented in this thesis is partly derived from the observation of product development processes implemented in traditional engineering disciplines. It can be argued that this approach represents a step forward in the evolution of software constmction based on components composition for it provides a simple and fluid components composition approach. The application and use of this composition approach reduces the need to retrofit and customize existing components as has been done traditionally. The software constructor relies more and more on selective composition of enhanced modularity components which suit particular application requirements. Moreover, this approach leads to additional secondary benefits which can be exploited in the use of fine-grained testing and in conducting various component measurements that ultimately benefit the software constmction process as a whole

Deep Learning at Scale with Nearest Neighbours Communications

As deep learning techniques become more and more popular, there is the need to move these applications from the data scientist’s Jupyter notebook to efficient and reliable enterprise solutions. Moreover, distributed training of deep learning models will happen more and more outside the well-known borders of cloud and HPC infrastructure and will move to edge and mobile platforms. Current techniques for distributed deep learning have drawbacks in both these scenarios, limiting their long-term applicability. After a critical review of the established techniques for Data Parallel training from both a distributed computing and deep learning perspective, a novel approach based on nearest-neighbour communications is presented in order to overcome some of the issues related to mainstream approaches, such as global communication patterns. Moreover, in order to validate the proposed strategy, the Flexible Asynchronous Scalable Training (FAST) framework is introduced, which allows to apply the nearest-neighbours communications approach to a deep learning framework of choice. Finally, a relevant use-case is deployed on a medium-scale infrastructure to demonstrate both the framework and the methodology presented. Training convergence and scalability results are presented and discussed in comparison to a baseline defined by using state-of-the-art distributed training tools provided by a well-known deep learning framework

An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part A: The core ingredients

Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. The goal of Artificial Intelligence is focused on developing computational approaches to intelligent behavior. This goal is so broad - covering virtually all aspects of human cognitive activity - that substantial confusion has arisen as to the actual nature of AI, its current status and its future capability. This volume, the first in a series of NBS/NASA reports on the subject, attempts to address these concerns. Thus, this report endeavors to clarify what AI is, the foundations on which it rests, the techniques utilized, applications, the participants and, finally, AI's state-of-the-art and future trends. It is anticipated that this report will prove useful to government and private engineering and research managers, potential users, and others who will be affected by this field as it unfolds

Functional partitioning of multi-processor architectures

Many real-time computations such as process control and robotic applications may be naturally distributed in a functional manner. One way of ensuring good performance, reliability and security of operation is to map or distribute such tasks onto a distributed, multi-processor system. The time-critical task is thus functionally partitioned into a set of cooperating sub-tasks. These sub-tasks run concurrently and asynchronously on different nodes (stations) of the system. The software design and support of such a functional distribution of sub-tasks (processes) depends on the degree of interaction of these processes among the different nodes. [Continues.

Computer science: Key to a space program renaissance. The 1981 NASA/ASEE summer study on the use of computer science and technology in NASA. Volume 2: Appendices

Adoption of an aggressive computer science research and technology program within NASA will: (1) enable new mission capabilities such as autonomous spacecraft, reliability and self-repair, and low-bandwidth intelligent Earth sensing; (2) lower manpower requirements, especially in the areas of Space Shuttle operations, by making fuller use of control center automation, technical support, and internal utilization of state-of-the-art computer techniques; (3) reduce project costs via improved software verification, software engineering, enhanced scientist/engineer productivity, and increased managerial effectiveness; and (4) significantly improve internal operations within NASA with electronic mail, managerial computer aids, an automated bureaucracy and uniform program operating plans

Advancement in robot programming with specific reference to graphical methods

This research study is concerned with the derivation of advanced robot programming methods. The methods include the use of proprietary simulation modelling and design software tools for the off-line programming of industrial robots. The study has involved the generation of integration software to facilitate the co-operative operation of these software tools. The three major researcli'themes7of "ease of usage", calibration and the integration of product design data have been followed to advance robot programming. The "ease of usage" is concerned with enhancements in the man-machine interface for robo t simulation systems in terms of computer assisted solid modelling and computer assisted task generation. Robot simulation models represent an idealised situation, and any off-line robot programs generated from'them may contain'discrepancies which could seriously effect thq programs' performance; Calibration techniques have therefore been investigated as 'a method of overcoming discrepancies between the simulation model and the real world. At the present time, most computer aided design systems operate as isolated islands of computer technology, whereas their product databases should be used to support decision making processes and ultimately facilitate the generation of machine programs. Thus the integration of product design data has been studied as an important step towards truly computer integrated manufacturing. The functionality of the three areas of study have been generalised and form the basis for recommended enhancements to future robot programming systems

A generic architecture for interactive intelligent tutoring systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 07/06/2001.This research is focused on developing a generic intelligent architecture for an interactive tutoring system. A review of the literature in the areas of instructional theories, cognitive and social views of learning, intelligent tutoring systems development methodologies, and knowledge representation methods was conducted. As a result, a generic ITS development architecture (GeNisa) has been proposed, which combines the features of knowledge base systems (KBS) with object-oriented methodology. The GeNisa architecture consists of the following components: a tutorial events communication module, which encapsulates the interactive processes and other independent computations between different components; a software design toolkit; and an autonomous knowledge acquisition from a probabilistic knowledge base. A graphical application development environment includes tools to support application development, and learning environments and which use a case scenario as a basis for instruction. The generic architecture is designed to support client-side execution in a Web browser environment, and further testing will show that it can disseminate applications over the World Wide Web. Such an architecture can be adapted to different teaching styles and domains, and reusing instructional materials automatically can reduce the effort of the courseware developer (hence cost and time) in authoring new materials. GeNisa was implemented using Java scripts, and subsequently evaluated at various commercial and academic organisations. Parameters chosen for the evaluation include quality of courseware, relevancy of case scenarios, portability to other platforms, ease of use, content, user-friendliness, screen display, clarity, topic interest, and overall satisfaction with GeNisa. In general, the evaluation focused on the novel characteristics and performances of the GeNisa architecture in comparison with other ITS and the results obtained are discussed and analysed. On the basis of the experience gained during the literature research and GeNisa development and evaluation. a generic methodology for ITS development is proposed as well as the requirements for the further development of ITS tools. Finally, conclusions are drawn and areas for further research are identified