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A case-based reasoning and inductive learning approach for heuristic search.

机译：基于案例的推理和归纳学习方法用于启发式搜索。

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摘要

Knowledge-based problem solvers traditionally merge knowledge about a domain with several heuristics in an effort to control novel problem situations intelligently. In this dissertation, we use an inductive learning algorithm to obtain domain concepts. Then we use a Case Based Reasoning (CBR) approach to solve heuristic search problems using the domain concepts obtained from the inductive learning approach. The main idea behind CBR approach is to make use of the old solutions while solving a new problem. In this research we identify some properties of the heuristic search functions so that CBR approach can be used to solve them. We also perform an analysis to compare the results of CBR approach with traditional heuristic search techniques.; The above methodologies are then applied to a real world engineering problem, namely wastewater treatment design. This design can be viewed as obtaining an optimal sequence of treatment plants to remove the contaminants in the wastewater. We formulated the treatment problem as a heuristic search problem and used the inductive learning and case based reasoning approaches to design the treatment sequence. We also developed a neural network methodology to obtain the optimal sequence of treatment trains and compared the results from the heuristic approach and the neural network approach.
机译：基于知识的问题解决者传统上将有关领域的知识与几种试探法相结合，以努力智能地控制新颖的问题情况。本文采用归纳学习算法获得领域概念。然后，我们使用基于案例的推理（CBR）方法，使用从归纳学习方法获得的领域概念来解决启发式搜索问题。 CBR方法背后的主要思想是在解决新问题的同时利用旧解决方案。在这项研究中，我们确定了启发式搜索功能的某些属性，以便可以使用CBR方法来解决它们。我们还进行了分析，以比较CBR方法与传统启发式搜索技术的结果。然后将以上方法应用于实际的工程问题，即废水处理设计。该设计可视为获得去除废水中污染物的最佳处理设备顺序。我们将处理问题表述为启发式搜索问题，并使用归纳学习和基于案例的推理方法来设计处理序列。我们还开发了一种神经网络方法来获得最佳的治疗序列，并比较了启发式方法和神经网络方法的结果。

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