reinforcement learning as a method to design conversations

[dcsan]

rel wechaty/wishlist#43

We can try to plan a perfect path through a conversation ahead of time, and write out a script for our bots. This is "top down design"

But often the user will run the conversation in a completely different way. If they were talking to a real human agent, the conversation would flow in a different sequence.
Some authoring system such as rasa will start with this approach:
https://rasa.com/docs/rasa/writing-stories

But then try to use annotations of actual conversations to refine the conversation flow.
However, the current tools on the market really are quite un-unsable for this. RASA stories IMHO qucikly devolve to a huge mess that is impossible to view or reason about.

So this project would be a new start in trying to combine NLU conversation insights from "human in the loop" choices, or post-review of past conversations, with the top-down designed stories. The choices a human makes should affect future conversations in a probabalistic way

a simple prototype exists here, but it is not connected to any kind of NN model
https://dc.rik.ai/projects/convoai

[dcsan]

SOwC #30