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 in 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 will help to gradually build the humor models of the agent following state of the art Interactive Reinforcement Learning techniques.

We plan to work on this project for 4 months, resulting in an implementation of the platform, a first model for humor-enabled conversational agent and a publication of the obtained results and evaluations.

Output

Online game for collecting humorous interaction data

Humor models for conversational agents

Paper in International Conference of Journal related to AI and AI in Games

Project Partners:

  • Centre national de la recherche scientifique (CNRS), Brian Ravenet
  • Instituto Superior Técnico (IST), Rui Prada

Primary Contact: Brian Ravenet, LISN-CNRS (ex LIMSI-CNRS)