Dialog Management: Ho to migrate state-based approach to a story-based approach?

RASA NLU (intents and entities classification) is clear, simple and well implemented.

But one of the things makes RASA different from many competitors is machine-learning based dialog management (using stories and rules).

Myself (and I guess many of people that use non RASA framework) I used to manage task-oriented (conversational) workflows using the concept of state-machine. BTW I developed a small open source project NaifJs, that conceptualize a conversation as a sequence (a graph) of states, where each state is a set of valid intents involved in a conversation context.

So, what is not clear to me is ho to migrate a state-based (or context-based) approach to a story-based approach. Just to simplify/exemplify, I could immagine that given a " dialog flow" realized by conditional statements, I have to “serialize/duplicate” each “if” in multiple "then"stories.

Do you have already explained this problem in some articles? Maybe a gift for a new blog article/youtube workshop!



1 Like