This paper deals with the technological transfer knowledge and expertise stochastic analysis of environmental systems, as well as the incorporation of prior information in the stochastic analysis of environmental systems. In such a framework, an important challenge is the simplification, accessibility and enhancement of usability of stochastic methods. Intelligent computer systems for stochastic analysis of spatial environmental variates may be seen as generalizations of conventional programs in the domain. These generalizations contain, in addition to numerical capabilities, the knowledge and expertise required to guide, undertake, evaluate, and reason about operations based on stochastic techniques.

To provide the required knowledge transfer, the integration of knowledge-expertise for stochastic analysis with symbolic techniques developed in the field of artificial intelligence has been perused, and obtained by establishing a formal hierarchy-taxonomy of stochastic conceptual units. This approach captures both the basic notions of spatial stochastic methods and the practical intricacies involved, thus providing the basis for the development of intelligent systems in this domain.

The feasibility and abilities of intelligent systems are demonstrated by two experimental systems, implemented in LISP. The first is concerned with the inference of variograms, and the second with the estimation of pertinent environmental variates. The characteristics of both systems is demonstrated by reference to a case study.

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