Providing arguments in discussions based on the prediction of human argumentative behavior

Argumentative discussion is a highly demanding task. In order to help people in such situations, this paper provides an innovative methodology for developing an agent that can support people in argumentative discus-sions by proposing possible arguments to them. By ana-lyzing more than 130 human discussions and 140 ques-tionnaires, answered by people, we show that the well-established Argumentation Theory is not a good predic-tor of people’s choice of arguments. Then, we present a model that has 76 % accuracy when predicting peoples top three argument choices given a partial deliberation. We present the Predictive and Relevance based Heuris-tic agent (PRH), which uses this model with a heuris-tic that estimates the relevance of possible arguments to the last argument given in order to propose possible arguments. Through extensive human studies with over 200 human subjects, we show that peoples satisfaction from the PRH agent is significantly higher than from other agents that propose arguments based on Argumen-tation Theory, predict arguments without the heuristics or only the heuristics. People also use the PRH agent’s proposed arguments significantly more often than those proposed by the other agents