Methodology for DSS and Expert Systems Development Using Distributed Data Bases

Abstract

Artificial Intelligence (AI) has been characterized as the science of ill-structured problems — and many modern management decisions surely fall into that category (Jordan, 1987). One definition of AI is: “Artificial Intelligence is the machine emulation of human perception, learning, planning, reasoning and decision making” (Jordan, 1985). Following this definition we can consider Decision Support Systems (DSS) and Expert Systems (ES) as derived from AI technologies. On the other side DSS and ES are in our opinion a logical extension of the Information Systems, based on the new quality and features of the hardware/software development. An Information System (IS) gives the answer to the question “WHAT”, a DSS — to the question “WHAT–IF” and an ES answers to the question “IF–THEN”. In the same time the ES has the role of a bridge between IS and DSS. It processes user requests and different data, stores users decisions and using this information the ES can produce new alternatives, comparing different rules and facts. So that an ES should integrate a part of the IS and DSS function, extending them using AI techniques.