This paper reviews Machine Learning (ML), and extends and complements previous work (Kocabas, 1991; Kalkanis and Conroy, 1991). Although this paper focuses on inductive learning, it at least touches on a great many aspects of ML in general. In addition, incremental induction is also reviewed. Therefore, a general review of ML is presented, but specific detail which has been covered previously is omitted, although other relevant references are noted, and later material is commented upon.

A major task of organic chemistry is the synthesis of compounds of interest (“targets”) from available starting compounds. We address here the generation by computer of the best synthesis routes, or sequences of reactions, to such compounds. The number of possible routes in the search space is enormous and so the reasoning is examined in some detail, leading to a protocol for the operation of our program, SYNGEN, which has been under development for some years. It requires a new representation of the data – molecular structures and their reactions – which is digital in format and quite unlike the usual graphical depictions of organic chemistry. This is presented as a problem in artificial intelligence, and every effort has been made to present it with a minimum background of organic chemistry required of the reader.

The development of a knowledge-based design support system is a lengthy and costly process because various computational techniques necessary for intelligent design support are not readily available in a knowledge-based environment. The systematisation of design knowledge needs combined efforts from designers and knowledge engineers. Existing knowledge-based system development tools offer limited support to intelligent design support which require sophisticated knowledge engineering techniques in terms of knowledge representation, inference, control, truth maintenance and learning. In this paper, a knowledge-based architecture for intelligent design support is described. The existing knowledge-based design system architectures are reviewed first. Five key issues in intelligent design support using knowledge engineering techniques, i.e. design knowledge representation, structure of design knowledge base, intelligent control of design process, consistency and context management of design knowledge, and modelling of design collaboration are then discussed. These discussions provide a basis for a description of a knowledge-based design support system architecture which has been implemented in a Lisp-based environment and tested in two different domains. Current application of this architecture in the development of a design support system in the domain of mechanical engineering design at the Cambridge Engineering Design Centre is presented and evaluated.

Over the past years, two main approaches to computational intelligence have emerged: the symbolic and the non-symbolic approach. The perhaps most prominent methods of the symbolic approach are based on logic. Logical methods exhibit a series of desirable properties:

[bull ] Transparent representation of meaning

[bull ] Precise understanding of the meaning of statements (semantics).

[bull ] Sound reasoning methods.

[bull ] Explanation capabilities.

A special session on logical methods for computational intelligence was held at the 3rd Joint Conference on Information Sciences. The field of computational logic is so broad that it is impossible to review the main developments in an article. Therefore, in the following we will restrict attention to two areas that turned out to be the focus of the special session: automated reasoning, and reasoning with incomplete and changing information.

Artificial intelligence research in temporal reasoning focuses on designing automated solutions to complex problems in computation involving time. TIME-97, the 4th International Workshop on Temporal Representation and Reasoning, held in Daytona Beach, Florida — like the three workshops that preceded it — had the objective of creating an international forum for the exchange of information among the many researchers and knowledge engineers who are developing and applying techniques in temporal reasoning.

‘Uncertainty reasoning” refers in a general way to problems discussed by that subset of the AI community interested in representing and reasoning with knowledge that cannot be expressed as certainties. The range of problems discussed runs the gamut from fundamental philosophical inquiry into the nature of uncertainty and how (if at all) it can be measured and modelled, to practical performance issues arising from the (automatic) construction of real-world models and making inferences from such models.