动态多项选择（像向导）- 您将如何设计它？ （例如Schema、AI模型等）
这个问题可能可以分成多个问题，但这里是......
本质上，我想允许用户输入他们想要做的事情，并提供一个类似向导的界面来询问缺少的信息完成请求的查询。例如，假设用户输入：“斯普林菲尔德的天气怎么样？”
我们认识到用户对天气感兴趣，但可能是伊利诺伊州斯普林菲尔德或另一个州的斯普林菲尔德。后续问题是：
您想要斯普林菲尔德的天气状况如何？ 1 - 伊利诺伊州斯普林菲尔德 2 - 斯普林菲尔德，威斯康星州
您可能会想到一百万个请求缺少关键数据或其不明确的示例。假设用户想要的内容是可以理解的，但缺少完成请求所需的数据。
也许您可以追溯到询问用户想要做什么并“引导”他们进行查询。
这并不是接受任何输入并真正理解它的意义上的人工智能。我并不是指通过某种方式与用户进行对话。它是关于推断用户想要什么，检查是否有要提供的适用服务，识别所需的输入并将其覆盖在请求中缺少的内容之上，然后询问用户剩余的信息。就是这样！ :-)
您希望如何存储有关服务的信息？您将如何确定输入数据中缺少哪些内容？
我的想法： 使用正则表达式来识别清晰的信息。这些将与服务的参数相匹配。找出哪些参数没有匹配数据，并查找这些参数的相关问题。提出这些问题并获取答案。重新运行传递新捕获的数据的服务。这些问题将更加自由。
对于多项选择，识别歧义并搜索按可能性顺序排名的潜在匹配项（添加用户历史记录/偏好以帮助做出决定）。提供前 3 个作为选择。
思想赞赏。
干杯， 亨利
This question can probably be broken up into multiple questions, but here goes...
In essence, I'd like to allow users to type in what they would like to do and provide a wizard-like interface to ask for information which is missing to complete a requested query. For example, let's say a user types: "What is the weather like in Springfield?"
We recognize the user is interested in weather, but it could be Springfield, Il or Springfield in another state. A follow-up question would be:
What Springfield did you want weather for?
1 - Springfield, Il
2 - Springfield, Wi
You can probably think of a million examples where a request is missing key data or its ambiguous. Make the assumption the gist of what the user wants can be understood, but there are missing pieces of data required to complete the request.
Perhaps you can take it as far back as asking what the user wants to do and "leading" them to a query.
This is not AI in the sense of taking any input and truly understanding it. I'm not referring to having some way to hold a conversation with a user. It's about inferring what a user wants, checking to see if there is an applicable service to be provided, identifying the inputs needed and overlaying that on top of what's missing from the request, then asking the user for the remaining information. That's it! :-)
How would you want to store the information about services? How would you go about determining what was missing from the input data?
My thoughts:
Use regex expressions to identify clear pieces of information. These will be matched to the parameters of a service. Figure out which parameters do not have matching data and look up the associated question for those parameters. Ask those questions and capture answers. Re-run the service passing in the newly captured data. These would be more free-form questions.
For multiple choice, identify the ambiguity and search for potential matches ranked in order of likelihood (add in user history/preferences to help decide). Provide the top 3 as choices.
Thoughts appreciated.
Cheers,
Henry
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肯定是这样！您可以通过准确说明来跟进此内容：
推理是人工智能任何主题的核心。用户的意思是什么？用户想要什么？我应该获取什么信息？我如何解析该信息并确定答案是什么？
你本质上是在尝试设计一个最先进的人工智能系统，它使用 NLP 技术的组合来解析自然语言查询，然后使用（也许）学习算法来确定如何执行搜索，可能会遇到知识库，或者可能是谷歌（这也需要一个过程来解析返回的数据以找到答案）。
如果有任何方法可以限制输入的方式（即如何询问查询），那将会有所帮助。但随后您实际上将构建一个 Web 表单...这已经完成了一百万次。
简而言之，您正在尝试创建一个非常复杂的系统，但您明确不想使用任何相关技术。如果您尝试使用正则表达式来完成所有这些，祝您好运。因为那是一个又深又黑的兔子洞，我不想掉进去。
但如果你坚持的话，可以先找一本关于 NLP 的好书，因为无论如何你都必须从那里开始。
It most certainly is! You follow this up by stating exactly that:
Inference is at the heart of any topics in AI. What did the user mean? What did the user want? What information should I fetch? How do I parse that information and decide what the answer is?
You're essentially trying to design a state-of-the-art AI system, that uses a combination of NLP techniques to parse natural language queries and then using (maybe) a learning algorithm to determine how to perform the search, possibly hitting a knowledge base, or maybe Google (which also requires a process to parse the returned data to find the answer).
If there is any way you can constrain how input is entered (i.e. how the query is asked), that will help. But then you'll essentially be building a Web form... which has been done a million times over.
In short, you're attempting to create a very complex system but you explicitly don't want to use any of the relevant techniques. If you're attempting to use regexs to do all of this, good luck to you. Because that's one heck of a deep and dark rabbit hole into which I would not want to fall.
But if you insist, start by finding a good book on NLP, because that's where you'll have to start anyway.