  Most chatbot literature that focuses on improving the fluency and coherence
of a chatbot, is dedicated to making chatbots more human-like. However, very
little work delves into what really separates humans from chatbots -- humans
intrinsically understand the effect their responses have on the interlocutor
and often respond with an intention such as proposing an optimistic view to
make the interlocutor feel better. This paper proposes an innovative framework
to train chatbots to possess human-like intentions. Our framework includes a
guiding chatbot and an interlocutor model that plays the role of humans. The
guiding chatbot is assigned an intention and learns to induce the interlocutor
to reply with responses matching the intention, for example, long responses,
joyful responses, responses with specific words, etc. We examined our framework
using three experimental setups and evaluated the guiding chatbot with four
different metrics to demonstrate flexibility and performance advantages.
Additionally, we performed trials with human interlocutors to substantiate the
guiding chatbot's effectiveness in influencing the responses of humans to a
certain extent. Code will be made available to the public.
