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首页> 外文期刊>Progress in Oceanography >Knowledge-based Systems As Decision Support Tools In An Ecosystem Approach To Fisheries: Comparing A Fuzzy-logic And A Rule-based Approach
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Knowledge-based Systems As Decision Support Tools In An Ecosystem Approach To Fisheries: Comparing A Fuzzy-logic And A Rule-based Approach

机译：基于知识的系统作为渔业生态系统方法中的决策支持工具：比较模糊逻辑和基于规则的方法

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摘要

In an ecosystem approach to fisheries (EAF), management must draw on information of widely different types, and information addressing various scales. Knowledge-based systems assist in the decision-making process by summarising this information in a logical, transparent and reproducible way. Both rule-based Boolean and fuzzy-logic models have been used successfully as knowledge-based decision support tools. This study compares two such systems relevant to fisheries management in an EAF developed for the southern Benguela. The first is a rule-based system for the prediction of anchovy recruitment and the second is a fuzzy-logic tool to monitor implementation of an EAF in the sardine fishery. We construct a fuzzy-logic counterpart to the rule-based model, and a rule-based counterpart to the fuzzy-logic model, compare their results, and include feedback from potential users of these two decision support tools in our evaluation of the two approaches. With respect to the model objectives, no method clearly outperformed the other. The advantages of numerically processing continuous variables, and interpreting the final output, as in fuzzy-logic models, can be weighed up against the advantages of using a few, qualitative, easy-to-understand categories as in rule-based models. The natural language used in rule-based implementations is easily understood by, and communicated among, users of these systems. Users unfamiliar with fuzzy-set theory must "trust" the logic of the model. Graphical visualization of intermediate and end results is an important advantage of any system. Applying the two approaches in parallel improved our understanding of the model as well as of the underlying problems. Even for complex problems, small knowledge-based systems such as the ones explored here are worth developing and using. Their strengths lie in (ⅰ) synthesis of the problem in a logical and transparent framework, (ⅱ) helping scientists to deliberate how to apply their science to transdisciplinary issues that are not purely scientific, and (ⅲ) representing vehicles for delivering state-of-the-art science to those who need to use it. Possible applications of this approach for ecosystems of the Humboldt Current are discussed.
机译：在渔业生态系统方法（EAF）中，管理必须利用种类繁多的信息以及涉及各种规模的信息。基于知识的系统通过以逻辑，透明和可重现的方式汇总此信息来辅助决策过程。基于规则的布尔模型和模糊逻辑模型已成功用作基于知识的决策支持工具。这项研究在为本格拉南部开发的渔业生态系统方法中比较了两种与渔业管理有关的系统。第一个是基于规则的系统，用于预测an鱼的招募，第二个是用于监视沙丁鱼渔业中EAF实施的模糊逻辑工具。我们构建了基于规则的模型的模糊逻辑对应物，并构建了基于逻辑的模型的基于规则的对应物，比较它们的结果，并在评估这两种方法时包括了来自这两种决策支持工具的潜在用户的反馈。关于模型目标，没有任何方法明显优于其他方法。像在模糊逻辑模型中那样，对连续变量进行数字处理并解释最终输出的优点可以与在基于规则的模型中使用一些定性且易于理解的类别的优点权衡。这些系统的用户很容易理解并在基于规则的实现中使用自然语言。不熟悉模糊集理论的用户必须“信任”模型的逻辑。中间结果和最终结果的图形可视化是任何系统的重要优势。并行应用这两种方法改善了我们对模型以及潜在问题的理解。即使对于复杂的问题，基于小型知识的系统（例如此处探讨的系统）也值得开发和使用。他们的优势在于（ⅰ）在一个逻辑透明的框架中综合该问题；（ⅱ）帮助科学家们思考如何将其科学应用于非纯粹科学的跨学科问题；（ⅲ）代表传递状态信息的工具最先进的科学给需要使用它的人。讨论了该方法在洪堡海流生态系统中的可能应用。

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