During the last 15 years, the ability to capture and distribute human decision-making skills on a desktop computer has created new possibilities for the management of many types of resources. The resource may be monetary, social, natural or economic. Computer programs that function with a certain degree of skill in solving problems are referred to as expert systems or knowledge systems. Natural resource management appears to be particularly well suited to these types of decision aids. As a result, there is a growing demand for personnel trained in knowledge system development. This guide attempts to cover the major areas which a conservation biologist or research manager requires in order to apply knowledge systems: knowledge acquisition; building a prototype; moving to a final system; knowledge validation; and implementation. Of particular interest is a section discussing the features of a resource management problem appropriate, or inappropriate, for an expert system application.
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AI and natural resource management. Knowledge-based systems: representation and search. Other knowledge system components. Planning the application. Knowledge acquisition. Designing the application. Programming knowledge systems in Prolog. An initial prototype using native PROLOG. A PROLOG toolkit approach to developing forest management knowledge-based systems; System evaluation and delivery. Appendix A: Roots of artificial intelligence. Bibliography. Glossary. Index.
Book by Schmoldt Daniel L Rauscher H Michael
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Da: Robinson Street Books, IOBA, Binghamton, NY, U.S.A.
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Hardback. Condizione: New. Authored by Daniel L. Schmoldt and H. Michael Rauscher, this hardcover volume offers a structured methodology for the planning, development, deployment, and evaluation of expert systems applied to natural resource management. The text addresses key phases such as system architecture, knowledge elicitation from domain experts, prototyping, and validation, providing guidance for resource managers and researchers tackling interdisciplinary problems where conventional mathematical models fall short. Published by Chapman & Hall in 1996, it reflects the broader integration of artificial intelligence techniques into environmental and conservation sciences during that period. Codice articolo NN-260429-shelf-7-004
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