Interactive Reinforcement Learning for Humorous Agents

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)

Main results of micro project:

The main result of this project will be the creation of an intelligent agent capable of playing a game – Cards Against Humanity- that involves matching sentences with humorous comebacks. The game requires that players be able to combine black and white cards to form the funniest joke possible. Therefore, the developing AI agent must be able to make funny jokes. Ultimately, this opens perspectives for the development of humor models in conversational AIs, a key social competence in our daily human interactions.

Contribution to the objectives of HumaneAI-net WPs

The micro-project produced for HumaneAI-net a dataset of annotated associations between black and white cards following the game design of Cards Against Humanity. By doing so, the micro-project led to the creation of a unique dataset of humorous associations between concepts, annotated in terms of different humor styles by the participants of the experiment. The preliminary analysis on how the dataset can be leveraged to build different humor models for conversational agents is particularly relevant for the tasks T3.3 and 3.4 of WP3. Additionally, the micro project aims at exploring how to refine the humor models through an interactive learning approach, particularly relevant for the task T1.3 of WP1.

Tangible outputs

• Dataset: Dataset – 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
  – Rui Prada
• Program/code: Online Game – A game of matching sentences with humorous comebacks against an agent (similar to the game Cards Against Humanity)
  – Ines Batina