Expert systems (ES) as a new and emerging technology of information processing and decision support are becoming increasingly useful tools in numerous application areas. Expert systems are man-machine systems that perform problem-solving tasks in a specific domain. They use rules, heuristics, and techniques such as first-order logic or semantic networks, to represent knowledge, together with inference mechanisms, in order to derive or deduce conclusions from stored and user-supplied information.

Application-and problem-oriented, rather than methodology-oriented, systems are most often hybrid or embedded systems, where elements of AI technology, and expert systems technology in particular, are combined with more classical techniques of information processing and approaches of operations research and systems analysis. Here traditional numerical data processing is supplemented by symbolic elements, rules, and heuristics in the various forms of knowledge representation.

Applications containing only small knowledge bases, of at best a few dozen to a hundred rules, can dramatically extend the scope of standard computer applications in terms of application domains, as well as in terms of an extended non-technical user community.

This review covers a basic introduction to what expert systems and AI methods are, what they can, and cannot do; the state of the art in applications in the field of water resources systems analysis and modelling and selected examples of expert and hybrid systems in the field that integrates simulation modelling, optimization and AI technology.