[TMP-066] Interactive Reinforcement Learning for Humorous Agents

Contact person: Brian Ravenet (brian.ravenet@limsi.fr) 

Internal Partners:

1. CNRS, Brian Ravenet, brian.ravenet@limsi.fr
2. INESC-ID, Rui Prada, rui.prada@tecnico.ulisboa.pt

This project aims at investigating the construction of humor models to enrich conversational agents through the help of interactive reinforcement learning approaches. Our methodology consists of deploying an online platform where passersby can play a game of matching sentences with humorous comebacks against an agent. The data collected from these interactions helps to gradually build the humor models of the agent following state of the art Interactive Reinforcement Learning techniques. Our work resulted in an implementation of the platform, a first model for humor-enabled conversational agent and a publication of the obtained results and evaluations.

Results Summary

The main result of this project is the creation of an intelligent agent capable of playing a game – Cards Against Humanity- that involves matching sentences with humorous comebacks. In order to achieve this, a dataset of 1712 jokes, rated on a scale of 1 to 9 in terms of joke level, originality, positivity, entertainment, whether it makes sense and whether it is family-friendly, were collected and an online game was developed to serve as the foundation of the reinforcement mechanism.