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

该提案结合了强大的统计能力， 基于规则的专家系统的开发 帮助解决农业问题的系统 生产问题。 预计在发展方面将取得普遍进展 更好地处理和表达不确定性的方法 专家系统模型 具体进展预计在 开发改进的工具来保护大豆作物免受 害虫是农业生产研究的重要课题， 并将决策分析技术的使用扩展到 农业部门。 该项目将采用 贝叶斯概率推理及其表示 通过影响图的随机推理网络， 将有助于构建问题并探索依赖关系 关键变量之间。 该项目的成功将有助于 论证使用计算机专家系统的可行性 系统作为决策辅助工具，以解决生产决策问题 涉及风险和不确定性。

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.