Automatic generation of humor for social robots (AGENTS)

Type: National Project

Duration: from 2021 Jan 01 to 2021 Dec 31

Financed by: FCT

Prime Contractor: INESC-ID (Other)

The central idea of this proposal is that (a) humour is an important feature in human communication that can be leveraged to create more naturalistic and lifelike interactions with robots and (b) humor potentialities can be increase through the delivery of user-personalized humour in naturalistic settings. In particular, we argue that psychological models of humor and its’ everyday functions can be of use when attempting to create a top-down approach of humor that can be modelled to match each user’s preferences. We will use a 2x2 conceptualization of humour that involves the social function of humour (humour used to enhance oneself or used to enhance others) and the valence of the humoristic content (positive, negative) (Martin, 2003). Using this conceptualization, we propose the creation of a dataset of jokes and the application of supervised machine learning techniques that will allow us to extract and automatize multimodal humour delivery according to the style of humour of the user. The end-goal of this process would be the implementation of user personalized humoristic interactions in the context of a group card game involving more than one human and more than one robot. This is expected to lead to better interaction outcomes and increase the value perception of the robot, by contributing to greater user’s task enjoyment, more positive perception of the robots and intention to interact again with these social agents in the future.


  • Carnegie Mellon University (University) - Pittsburgh, PA , USA
  • ISCTE (University)

Principal Investigators