The place of expert systems in business now and over the next decade

Information technology has entered a new generation. In recent years, considerable interest has been focussed on the commercialisation of expert systems, which represent an important application of Artificial Intelligence in the field of Information Technology. Expert systems are now in a crucial stage of development because, although in business computerised systems are not new, expert systems still need time for their applicability and usefulness to be proved. The market for expert systems will not develop if such systems are unable to cope with the demanding applications of business; for example with top management problem-solving and decision-making. This thesis is principally concerned with determining the position of expert systems in business by looking at these major business related issues. [Continues.

DESIGNING EXPERT SYSTEMS IN A BUSINESS ENVIRONMENT

The integration of an ES into a business environment presents a different set of problems to the designer. First, it can be difficult to isolate and draw boundaries around the domain of the business problem. Second, there is often a need to generate a large number of expert decisions in a short period of time. That is, there can be many transactions requiring some expertise to process, such as applications for life insurance. Finally, there may exist a number of computerized transactions processing systems which interact with very large databases and there may be a need to integrate the ES with these existing systems. This paper discusses these general issues involved in developing expert systems for business applications, with particular examples drawn from the domain of insurance underwriting.Information Systems Working Papers Serie

A fuzzy expert system for small business loan processing

In recent years, managerial applications of artificial intelligence, especially in the area of financial services, has received considerable attention. In this paper, a fiizzy logic expert system is developed for approvals of small business loans. Previous studies have used non-fuzzv expert systems. But the fuzzification of the variables used in a business loan approval decisionmaking promises more efficient results. Furthermore, another distinct feature of this paper is its focus on small business loans. CubiCalc fuzzy expert system shell is used to develop the expert system. Knowledge acquisition is made using the resources and expertise of a Small Business Development Center. Tests to establish generalizability and validity of the results are conducted

Several New Metrics for Evaluation of Expert Systems Based on Service-Oriented Architecture

Modern expert systems have some limitations such as vulnerabilities, the unity of solution strategy, development problems, repair and maintenance. These limitations could be covered by Service-Oriented Architecture (SOA), due to the advantages and various applications of this architecture and its combination to expert systems. The main purpose of this paper is to survey service-oriented architecture to provide solutions for using this style in order to eliminate the short comings and optimize services in expert systems. In this paper, several metrics for evaluating expert systems based on service-oriented architecture are presented. These metrics are in six branches: agility, integrity, usability and reusability, business objectives, accessibility, offering new and applied services. For each metric some properties are presented which the expert systems show more powerful in evaluation.DOI:http://dx.doi.org/10.11591/ijece.v3i5.403

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

Assessing the Potential of Ubiquitous Computing for Improving Business Process Performance

The term “ubiquitous technology” refers to any technology that extends common objects with data processing capabilities, e.g. RFID systems, wireless sensor networks or networked embedded systems. In this study, we uncover the mechanisms by which these technologies contribute to an increased business process performance. We apply the theory of task-technology fit to establish a model of the impact of ubiquitous technologies on business process performance. Based on expert interviews in a large standard software company, the potential of ubiquitous technologies for enhancing performance in a number of generic business processes is explored. Furthermore, we illustrate how our findings can be applied to identify value-creating ubiquitous computing applications in companies

COUPLING EXPERT SYSTEMS WITH DATABASE MANAGEMENT SYSTEMS

The combined use of Database Management Systems (DBMS) and Artificial Intelligence-based Expert Systems (ES) is potentially very valuable for modern business applications. The large body of facts usually required in business information systems can be made available to an ES through an existing commercial DBMS. Furthermore, the DBMS itself can be used more intelligently and operated more efficiently if enhanced with ES features. However, the implementation of a DBMS-ES cooperation is very difficult. We explore practical benefits of the cooperative use of DBMS and ES, as well as the research challenges it presents. Strategies for providing data from a DBMS to an ES are given; complementary strategies for providing intelligence from an ES to a DBMS are also presented. Finally, we discuss architechural issues such as degree of coupling, and combination with quantitative methods. As an illustration, a research effort at New York University to integrate a logic-based business ES with a relational DBMS is described.Information Systems Working Papers Serie

Eptistomological Aspects of Knowledge-Based Decision Support Systems

Knowledge-based decision support applications differ from those typical of artificial intelligence expert systems in their open-ended, evolutionary character and need to coordinate with other systems resources, such as organizational databases and quantitative analysis routines. While knowledge representation machinery is becoming available, the corresponding formalization of managerial/administrative knowledge needed for DSS application is still lacking. This entails problems of an epistomological nature, identifying the foundational concepts of business. An abstract framework based on formal languages and denotational semantics is proposed, and ontological issues are identified

DESIGNING EXPERT SYSTEMS IN A BUSINESS ENVIRONMENT

The integration of an ES into a business environment presents a different set of problems to the designer. First, it can be difficult to isolate and draw boundaries around the domain of the business problem. Second, there is often a need to generate a large number of expert decisions in a short period of time. That is, there can be many transactions requiring some expertise to process, such as applications for life insurance. Finally, there may exist a number of computerized transactions processing systems which interact with very large databases and there may be a need to integrate the ES with these existing systems. This paper discusses these general issues involved in developing expert systems for business applications, with particular examples drawn from the domain of insurance underwriting.Information Systems Working Papers Serie

SHELL-BASED EXPERT SYSTEMS IN BUSINESS: A RETURN ON INVESTMENT PERSPECTIVE

This paper examines an important issue emerging in information systems management--the decision to proceed with an expert system application in a business setting. The focus is knowledge based systems at the lower end of the complexity spectrum--small, very focused systems that can be implemented by the use of shell-based development environments. This group represents the majority of expert systems that are currently being implemented and has some characteristics quite different from the larger systems. A classification scheme is suggested to differentiate three levels of ES development, from multi-million dollar life cycle cost ES environments to those that are in the low five figure range. The Low End segment of the range, the focus of this paper, is characterized by lower unit costs, powerful development tools and a large number of small, successful applications. The important role of Low End systems is discussed, with particular emphasis on their relatively high yield in standalone applications. Such systems do not meet the AI demands of moderately or very complex problems but there is a surprising breadth in their use. A group of key success factors for Low End systems is proposed, based on a synthesis of the applications literature. To operationalize these factors, three actual cases using Low End technology--from marketing, government and agribusiness-- are briefly described. Low End systems are not all gain. Their low unit costs can often mask the risks of proceeding headlong into an application without careful examination of the variables that can predict successful results. An agenda for action is offered for specific management policies for the planning of knowledge-based applications