TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system

The Software Management Environment (SME)

The Software Management Environment (SME) is a research effort designed to utilize the past experiences and results of the Software Engineering Laboratory (SEL) and to incorporate this knowledge into a tool for managing projects. SME provides the software development manager with the ability to observe, compare, predict, analyze, and control key software development parameters such as effort, reliability, and resource utilization. The major components of the SME, the architecture of the system, and examples of the functionality of the tool are discussed

Automated support for experience-based software management

To effectively manage a software development project, the software manager must have access to key information concerning a project's status. This information includes not only data relating to the project of interest, but also, the experience of past development efforts within the environment. This paper describes the concepts and functionality of a software management tool designed to provide this information. This tool, called the Software Management Environment (SME), enables the software manager to compare an ongoing development effort with previous efforts and with models of the 'typical' project within the environment, to predict future project status, to analyze a project's strengths and weaknesses, and to assess the project's quality. In order to provide these functions the tool utilizes a vast corporate memory that includes a data base of software metrics, a set of models and relationships that describe the software development environment, and a set of rules that capture other knowledge and experience of software managers within the environment. Integrating these major concepts into one software management tool, the SME is a model of the type of management tool needed for all software development organizations

Expert systems built by the Expert: An evaluation of OPS5

Two expert systems were written in OPS5 by the expert, a Ph.D. astronomer with no prior experience in artificial intelligence or expert systems, without the use of a knowledge engineer. The first system was built from scratch and uses 146 rules to check for duplication of scientific information within a pool of prospective observations. The second system was grafted onto another expert system and uses 149 additional rules to estimate the spacecraft and ground resources consumed by a set of prospective observations. The small vocabulary, the IF this occurs THEN do that logical structure of OPS5, and the ability to follow program execution allowed the expert to design and implement these systems with only the data structures and rules of another OPS5 system as an example. The modularity of the rules in OPS5 allowed the second system to modify the rulebase of the system onto which it was grafted without changing the code or the operation of that system. These experiences show that experts are able to develop their own expert systems due to the ease of programming and code reusability in OPS5

Implementing a real time reasoning system for robust diagnosis

The objective of the Thermal Control System Automation Project (TCSAP) is to develop an advanced fault detection, isolation, and recovery (FDIR) capability for use on the Space Station Freedom (SSF) External Active Thermal Control System (EATCS). Real-time monitoring, control, and diagnosis of the EATCS will be performed with a knowledge based system (KBS). Implementation issues for the current version of the KBS are discussed

Towards expert systems for improved customer services using ChatGPT as an inference engine.

By harnessing both implicit and explicit customer data, companies can develop a more comprehensive understanding of their consumers, leading to better customer engagement and experience, and improved loyalty. As a result, businesses have embraced many AI technologies, including chatbots, sentiment analysis, voice assistants, predictive analytics, and natural language processing, within customer services and e-commerce. The arrival of ChatGPT, a state-of-the-art deep learning model trained with general knowledge in mind, has brought about a paradigm shift in how companies approach AI applications. However, given that most business problems are bespoke and require specialised domain expertise, ChatGPT needs to be aligned with the requisite task-oriented ability to solve these issues. This paper presents an iterative procedure that incorporates expert system development process models and prompt engineering, in the design of descriptive knowledge and few-shot prompts, as are necessary for ChatGPT-powered expert systems applications within customer services. Furthermore, this paper explores potential application areas for ChatGPT-powered expert systems in customer services, presenting opportunities for their effective utilisation in the business sector

Information Technology in The Learning Economy -Challenges for Developing Countries

This paper inquires how the concept of the "learning economy" can be applied to the requirements of developing countries. The main purpose is to develop an analytical framework to better understand how learning and capability formation can foster industrial upgrading. Special emphasis is given to te spread of information technology (IT). We inquire under what conditions developing countries can use this set of generic technologies to improve their learning capabilities. We argue that information technology should not be regarded as a potential substitute for human skills and tacit knowledge. Instead, its main role should be to support the formation and use of tacit knowledge. In the paper we compare two stylised models of the learning economy, the Japanese versus the American model. The Japanese model is explicit in its promotion and exploitation of tacit knowledge, while the American model is driven by a permanent urge to reduce the importance of tacit knowledge and to transform it into information - that is into explicit, 4 well structured and codified knowledge. We show that each of these models has peculiar strengths and weaknesses. Developing countries need to develop their own hybrid forms of institutions that combine the advantages of both models in a way that is appropriate to their idiosyncratic needs and capabilities.information technology; learning; learning economy; knowledge; capabilities; networks; developing countries; economic development; industrial upgrading