Institute for Artificial Intelligence

Argumentation Games


When two people are debating the merits of an analogical argument, it is very easy for the discussion to go off topic and to lose sight of what is relevant to the argument being discussed. Our debate framework WG-A addresses this by using a limited interface where only a select set of "moves" can be made, all of which are relevant to the analogical argument, such that the entire exchange can be moderated by an impartial artificially intelligent system. We have shown that the use of WG-A not only ensures relevant argumentative moves, but that it may also encourage temporary increases in critical and scientific reasoning ability of the participants.

We have shown that argumentation games like WG-A can be used in legal reasoning as well, such as in the establishing of the relevance of evidence. WG-A may also have use as a tool for introspective reasoning (reasoning about one's own beliefs) and discovery of one's own implicit biases. Ph.D. student Lindsay Fields is the lead researcher on a recently-funded project to use WG-A and other argumentation games to explore anti-black implicit biases.

For more details, please visit the AHMR Lab website.


Licato, J., Cooper, M. (2019). "Evaluating Relevance in Analogical Arguments through Warrant-based Reasoning", European Conference on Argumentation (ECA 2019).

Licato, J., Cooper, M. (2020). "Assessing Evidence Relevance By Disallowing Direct Assessment", 12th Conference of the Ontario Society for the Study of Argumentation.

Cooper, M., Fields, L., Badilla, M., Licato, J. (2020). "WG-A: A Framework for Exploring Analogical Generalization and Argumentation", In Proceedings of the 42nd Cognitive Science Society Conference (CogSci 2020).