In every professional field there are large bodies of information acquired through study and experience by practitioners. In many fields, individuals can be identified whose performance consistently approaches the best. The goal of expert systems technology is to embody the experts' knowledge in some field within a computer. Then, the computer can act as an expert consultant for non-expert professionals or laymen. Existing systems, such as MYCIN [2], for diagnosing blood infections, or PROSPECTOR [3], for evaluating field sites for minable mineral deposits, can perform at a level exceeding that of the average practitioner in the field. These systems typically run on large, time-shared computers. There are two components to an expert system: the expert knowledge itself, and a'core' system for manipulating that knowledge and interacting with the user. General methodologies have been developed for encoding expert knowledge; the encoding is typically done by a computer scientist in close collaboration with an expert or experts from the field of specialization.

A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.

A computer program has been written that successfully discovers syntheses for complex organic chemical moleculeB. The definition of the search space and strategies for heuristic search are described in this paper. It is not growing like a tree... ...In small proportions we just beauties see; - Ben Jonson. Introduction The design of application of artificial intelligence to a scientific task such as Organic Chemical Synthesis was the topic of a Doctoral Thesis completed in the summer of 197I. Chemical synthesis in practice involves i) the choice of molecule to be synthesized; ii) the formulation and specification of a plan for synthesis (involving a valid reaction pathway leading from commercial or readily available compounds to the target compounds with consideration of feasibility regarding the purposes of synthesis); iii) the selection of specific individual steps of reaction and their temporal ordering for execution; iv) the experimental execution of the synthesis and v) the redesign of syntheses, if necessary, depending upon the experimental results. In contrast to the physical synthesis of the molecule, the activity in ii) above can be termed the'formal synthesis'. This development of the specification of syntheses involves no laboratory technique and is carried out mainly on paper and in the minds of chemists (and now within a computer's memory!). Importance and Difficulty of Chemical Synthesis The importance of chemical synthesis is undeniable and there is emphatic testimony to the high regard held by scientists for synthesis chemists.

A minimal:robot,Icnown as Freddy, has been constructed with the aim of connecting a usable device online to the Department's lc L 4130, under the Multi-Pop time-sharing system, and discovering the snags. (See figure 1). Various technical problems arise when such a device runs free. It is much easier to anchor it and allow it to push its world about. Our present world is a three-foot diameter sandwich of hardboard and polystyrene which is light and rigid.

"A computer program has been written which can formulate hypotheses from a given set of scientific data. The data consist of the mass spectrum and the empirical formula of an organic chemical compound. The hypotheses which are produced describe molecular structures which are plausible explanations of the data. The hypotheses are generated systematically within the program's theory of chemical stability and within limiting constraints which are inferred from the data by heuristic rules. The program excludes hypotheses inconsistent with the data and lists its candidate explanatory hypotheses in order of decreasing plausibility. The computer program is heuristic in that it searches for plausible hypotheses in a small subset of the total hypothesis space according to heuristic rules learned from chemists."In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 209-254. Edinburgh University Press