Uncertainty in Expert Systems with Multiple Sources of Information Application to Agricultural Production

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This proposal combines strong capabilities in statistical modeling and simulation with the development of rule-based expert systems that will assist in the solution of agricultural production problems. General progress is expected in developing better ways to handle and express uncertainty in the context of expert systems models. Specific progress is expected in developing improved tools for protecting soybean crops from pests, an important issue in agricultural production research, and in extending the use of decision analytic techniques to the agricultural sector. The project will employ the framework of Bayesian probabilistic inference and the representation of stochastic inferential networks through influence diagrams, which will help to structure the problem and explore dependencies between key variables. The success of the project should help to demonstrate the feasibility of using computer-based expert systems as decision aids to address production decisions involving risk and uncertainty.