Advances in knowledge-based software engineering

The underlying hypothesis of this work is that a rigorous and comprehensive software reuse methodology can bring about a more effective and efficient utilization of constrained resources in the development of large-scale software systems by both government and industry. It is also believed that correct use of this type of software engineering methodology can significantly contribute to the higher levels of reliability that will be required of future operational systems. An overview and discussion of current research in the development and application of two systems that support a rigorous reuse paradigm are presented: the Knowledge-Based Software Engineering Environment (KBSEE) and the Knowledge Acquisition fo the Preservation of Tradeoffs and Underlying Rationales (KAPTUR) systems. Emphasis is on a presentation of operational scenarios which highlight the major functional capabilities of the two systems

Prototype software reuse environment at Goddard Space Flight Center

The Goddard Space Flight Center (GSFC) work is organized into four phases and includes participation by a contractor, CTA, Inc. The first phase was an automation study, which began with a comprehensive survey of software development automation technologies. Eight technical areas were analyzed for goals, current capabilities, and obstacles. The study documented current software development practice in GSFC Mission Operations and Data Systems Directorate, and presented short- and long-term recommendations that included focus on reuse and object-oriented development. The second phase, which has been completed, developed a prototype reuse environment with tools supporting object-oriented requirements analysis and design. This phase addressed the operational concept of software reuse, i.e., it attempted to understand how software can be reused. This environment has two semantic networks: object and keywords, and includes automated search, interactive browsing and a graphical display of database contents. Phase 3 was a domain analysis of Payload Operations Control Center (POCC) software. The goal in this phase was to create an initial repository of reusable components and techniques. Seven existing Operations Control Centers at GSFC were studied, but the domain analysis proved to be very slow. A lesson learned from this was that senior people who understand the environment and the functionality of the area are needed to perform successful domain analyses

Distributed, cooperating knowledge-based systems

Some current research in the development and application of distributed, cooperating knowledge-based systems technology is addressed. The focus of the current research is the spacecraft ground operations environment. The underlying hypothesis is that, because of the increasing size, complexity, and cost of planned systems, conventional procedural approaches to the architecture of automated systems will give way to a more comprehensive knowledge-based approach. A hallmark of these future systems will be the integration of multiple knowledge-based agents which understand the operational goals of the system and cooperate with each other and the humans in the loop to attain the goals. The current work includes the development of a reference model for knowledge-base management, the development of a formal model of cooperating knowledge-based agents, the use of testbed for prototyping and evaluating various knowledge-based concepts, and beginning work on the establishment of an object-oriented model of an intelligent end-to-end (spacecraft to user) system. An introductory discussion of these activities is presented, the major concepts and principles being investigated are highlighted, and their potential use in other application domains is indicated

Prototype fault isolation expert system for spacecraft control

A program which illustrates how an expert system might perform fault handling for a satellite propulsion subsystem is discussed. The program runs on a VAX 11/780 under VMS with a VT100 interface and is written in FRANZ/LISP. The program presents a series of menus to the user. Once the user selects from the menu, the program simulates the behavior of the hydrazine propulsion system under the fault condition, detects the presence of the fault, and then determines what set of actions to perform in implementing the fault workaround

An application of machine learning to the organization of institutional software repositories

Software reuse has become a major goal in the development of space systems, as a recent NASA-wide workshop on the subject made clear. The Data Systems Technology Division of Goddard Space Flight Center has been working on tools and techniques for promoting reuse, in particular in the development of satellite ground support software. One of these tools is the Experiment in Libraries via Incremental Schemata and Cobweb (ElvisC). ElvisC applies machine learning to the problem of organizing a reusable software component library for efficient and reliable retrieval. In this paper we describe the background factors that have motivated this work, present the design of the system, and evaluate the results of its application

Knowledge From Pictures (KFP)

The old maxim goes: 'A picture is worth a thousand words'. The objective of the research reported in this paper is to demonstrate this idea as it relates to the knowledge acquisition process and the automated development of an expert system's rule base. A prototype tool, the Knowledge From Pictures (KFP) tool, has been developed which configures an expert system's rule base by an automated analysis of and reasoning about a 'picture', i.e., a graphical representation of some target system to be supported by the diagnostic capabilities of the expert system under development. This rule base, when refined, could then be used by the expert system for target system monitoring and fault analysis in an operational setting. Most people, when faced with the problem of understanding the behavior of a complicated system, resort to the use of some picture or graphical representation of the system as an aid in thinking about it. This depiction provides a means of helping the individual to visualize the bahavior and dynamics of the system under study. An analysis of the picture augmented with the individual's background information, allows the problem solver to codify knowledge about the system. This knowledge can, in turn, be used to develop computer programs to automatically monitor the system's performance. The approach taken is this research was to mimic this knowledge acquisition paradigm. A prototype tool was developed which provides the user: (1) a mechanism for graphically representing sample system-configurations appropriate for the domain, and (2) a linguistic device for annotating the graphical representation with the behaviors and mutual influences of the components depicted in the graphic. The KFP tool, reasoning from the graphical depiction along with user-supplied annotations of component behaviors and inter-component influences, generates a rule base that could be used in automating the fault detection, isolation, and repair of the system

An Agent Community Supporting Orbit Determination

Abstract During the past year we have been engaged in an effort to develop a prototype community of fine-grained agents to support the orbit determination process for the SOHO [1] mission. This paper presents a high-level overview of what was accomplished

Developing satellite ground control software through graphical models

This paper discusses a program of investigation into software development as graphical modeling. The goal of this work is a more efficient development and maintenance process for the ground-based software that controls unmanned scientific satellites launched by NASA. The main hypothesis of the program is that modeling of the spacecraft and its subsystems, and reasoning about such models, can--and should--form the key activities of software development; by using such models as inputs, the generation of code to perform various functions (such as simulation and diagnostics of spacecraft components) can be automated. Moreover, we contend that automation can provide significant support for reasoning about the software system at the diagram level

Towards an Autonomic Cluster Management System (ACMS) with Reflex Autonomicity

Cluster computing, whereby a large number of simple processors or nodes are combined together to apparently function as a single powerful computer, has emerged as a research area in its own right. The approach offers a relatively inexpensive means of providing a fault-tolerant environment and achieving significant computational capabilities for high-performance computing applications. However, the task of manually managing and configuring a cluster quickly becomes daunting as the cluster grows in size. Autonomic computing, with its vision to provide self-management, can potentially solve many of the problems inherent in cluster management. We describe the development of a prototype Autonomic Cluster Management System (ACMS) that exploits autonomic properties in automating cluster management and its evolution to include reflex reactions via pulse monitoring

Describing intelligent agent behaviors

The development of new intelligent agents requires an interdisciplinary approach to programming. The initial challenge is to describe the desired agent behaviors and abilities without necessarily committing the agent development project to one particular programming language. What are the appropriate linguistic and logical tools for creating a top level, unambiguous, program-independent, and consistent description of the functions and behaviors of the agent? And how can that description then be translated easily into one of a number of program languages? This article provides a case study of the application of a simple Belief, Desire, and Intention (EDI) first order logic to a complex set of agent functions of a theoretical community of intelligent nano-spacecraft. The basic research was conducted at NASA-GSFC (Greenbelt), Advanced Architecture Branch, during the summer of 2001. The simple examples of applied BDI logic presented here suggest broad application in agent software development