Computational modelling of scientific discovery is emerging as an important research field in artificial intelligence. Various computational systems modelling different aspects of scientific research and discovery have been developed. This paper looks at some of these models in order to examine how knowledge is organized in such systems, what forms of representation they have, how their methods of learning and representation are integrated, and the effects of representation on learning. The paper also describes the achievements and shortcomings of these systems, and discusses the obstacles in developing more comprehensive models.

This paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.

In the coming years, scheduling will be an increasingly important activity in the manufacturing and aerospace industries, especially at the US National Aeronautics and Space Administration (NASA). Expert systems have successfully been used to aid in the scheduling function. This paper surveys some of the conflict resolution approaches used in building expert scheduling systems, and then examines the feasibility of using an expert systems approach for scheduling activities and resolving conflicts for experimenters to use NASA Goddard-supported satellites. Recommendations for further reading in expert scheduling systems are given.