推理引擎与决策树 [英] Inference engines vs Decision trees

问题描述

我正在使用带有推理引​​擎(正向链接)的专家系统，我想解释一下为什么它比使用非常简单的概念的决策树更好. (在一种特定情况下)

I am using an expert system with an inference engine (forward chaining) and I would like to explain why it is better than a decision tree using very simple concepts. (in one particular situation)

我知道在stackoverflow上有一个类似的问题，但这不是我正在寻找答案.

I know there is a similar question on stackoverflow but it's not the answer I'm looking for.

这是我的问题:

对于客户关系管理，我正在使用许多不同的业务规则(这些规则会引发对话规则)来帮助客户对一种产品做出决定. 注意:规则会频繁添加(每天2条).

For Customer Relation Management, I am using lot of different business rules (that induce dialog rules) to help the customer make a decision on one product. Note: Rules are added frequently (2 per days).

客户在获得答案之前先回答了一系列问题.将业务规则与对话规则混合在一起，可使生成的调查表看起来像由最佳决策树生成的调查表.即使隐藏的推理完全不同.

The customer answers a series of questions before getting his answer. The business rules mixed with the dialog rules makes the resulting questionnaire looks like the one that would be generated by a optimal decision Tree. Even though the hidden reasonning is completely different.

与这种情况下的决策树相比，我想知道在可伸缩性，健壮性，复杂性和效率方面，支持(或可能反对)推理引擎的主要论点是什么.

I would like to know what are the main arguments in favor (or maybe against) of the inference engine in terms of scalability, robustness, complexity and efficiency compared to a decision tree in such a case.

我已经有了一些主意，但是由于我需要说服某人，就好像我没有足够的论据.

I already have some ideas, but since I need to convince someone it's like I never have enough arguments.

预先感谢您的想法，如果您能建议我撰写有关该主题的优秀论文，我将不胜感激.

Thanks in advance for your ideas and I would be happy if you could advise me good papers to read on this subject.

推荐答案

正向链推理引擎支持完整的一阶逻辑规范(转换为if-then规则)，而决策树只能将集合分解为特定的子集.如果您同时使用这两种方法来确定用户想要的汽车，则可以在一阶逻辑中说(

Forward chaining inference engines support specifications in full first-order logic (translated to if-then rules), while decision trees can only march down a set to a specific subset. If you're using both for, say, determining what car a user wants, then in first-order logic you can say (CHR syntax; <=> replaces LHS by RHS):

user_likes_color(C), available_color(C) <=> car_color(C).

除了确定用户所需汽车品牌/类型的所有规则外，推理引擎还将选择颜色以及其他属性.

in addition to all the rules that determine the brand/type of car the user wants, and the inference engine will pick the color as well as the other attributes.

对于决策树，您必须为颜色设置额外的树.只要颜色不与其他属性交互就可以，但是一旦它们与其他属性发生相互作用，您就被搞砸了:除了那些与其他属性冲突的颜色之外，您可能必须为每种颜色复制整个树.还可以修改树.

With decision trees, you'd have to set up an extra tree for the color. That's okay as long as color doesn't interact with other properties, but once they do, you're screwed: you may have to replicate the entire tree for every color except those colors that conflict with other properties, where you'd need to also modify the tree.

(我承认颜色是一个非常愚蠢的例子，但我希望它能使想法得到理解.)

(I admit color is a very stupid example, but I hope it gets the idea across.)

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