为数据科学研究项目选择聊天机器人框架并了解开发和部署的隐性成本？
问题是关于在一项研究中使用聊天机器人框架，人们希望衡量基于规则的决策过程随着时间的推移的改进。 例如，我们想了解如何使用最少的引导问题和患者互动来改进医疗状况识别（和治疗）的过程。
医生可以将病情制定成工作流程规则；此类研究的可能技术方法是开发一个可供患者访问的应用程序或网站，他们可以在其中询问预定义的基于规则的聊天机器人将解决的自由文本问题。在研究过程中，将有一名医生监视收集的数据并改进规则和可能的响应（并且当工作流程到达死胡同时也提供新的响应），我们确实计划收集对话并应用机器学习来生成随着时间的推移，改进了工作流程树（和问题），但是计划是离线进行任何数据分析和处理，无意构建完整的产品。 这是一项低预算的学院学习，博士生具有良好的开发技能和数据科学知识（Python），并将由一名从事工程方面工作的同学陪同。向数据科学家推荐的对话式人工智能选项之一是 RASA。
在过去的几天里，我花时间阅读和使用了几个聊天机器人解决方案：RASA、Botpress，还查看了 Dialogflow 并阅读了大量的比较材料，这使其更具挑战性。
从互联网上的消息来看，RASA 可能更适合数据科学项目，但是，如果能够了解真正的学习曲线以及人们可以期望多快拥有一个工作机器人，尤其是一个机器人，那就太好了那就得不断更新规则。
很少有事情需要澄清，我们确实有数据来生成问题并与医生联系以提高质量，似乎我们需要一种方法来向参与者介绍多种选择并提供答案（不仅仅是自由文本），正在研究中方面也没有必要与任何特定的大型提供商（即谷歌、亚马逊或微软）保持一致，除非它有好处，重要的考虑因素是时间、金钱和灵活性，我们希望在几周内有一个工作方法（并且不断改进）整个实验将运行不超过3-4个月。我们确实需要能够提取所有数据。我们不确定哪个渠道最适合进行此类研究 WhatsApp？网站？其他？其中涉及哪些复杂性？
任何关于处理聊天机器人的挑战和考虑因素的想法都是有价值的。
The question is about using a chat-bot framework in a research study, where one would like to measure the improvement of a rule-based decision process over time.
For example, we would like to understand how to improve the process of medical condition identification (and treatment) using the minimal set of guided questions and patient interaction.
Medical condition can be formulated into a work-flow rules by doctors; possible technical approach for such study would be developing an app or web site that can be accessed by patients, where they can ask free text questions that a predefined rule-based chat-bot will address. During the study there will be a doctor monitoring the collected data and improving the rules and the possible responses (and also provide new responses when the workflow has reached a dead-end), we do plan to collect the conversations and apply machine learning to generate improved work-flow tree (and questions) over time, however the plan is to do any data analysis and processing offline, there is no intention of building a full product.
This is a low budget academy study, and the PHD student has good development skills and data science knowledge (python) and will be accompanied by a fellow student that will work on the engineering side. One of the conversational-AI options recommended for data scientists was RASA.
I invested the last few days reading and playing with several chat-bots solutions: RASA, Botpress, also looked at Dialogflow and read tons of comparison material which makes it more challenging.
From the sources on the internet it seems that RASA might be a better fit for data science projects, however it would be great to get a sense of the real learning curve and how fast one can expect to have a working bot, and the especially one that has to continuously update the rules.
Few things to clarify, We do have data to generate the questions and in touch with doctors to improve the quality, it seems that we need a way to introduce participants with multiple choices and provide answers (not just free text), being in the research side there is also no need to align with any specific big provider (i.e. Google, Amazon or Microsoft) unless it has a benefit, the important consideration are time, money and felxability, we would like to have a working approach in few weeks (and continuously improve it) the whole experiment will run for no more than 3-4 months. We do need to be able to extract all the data. We are not sure about which channel is best for such study WhatsApp? Website? Other? and what are the involved complexities?
Any thoughts about the challenges and considerations about dealing with chat-bots would be valuable.
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