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Reinforcement Learning for Dialogue Game Based Argumentation

Communication between agents is an important feature for intelligent systems. One type of communication is the argumentative dialogue where both agents aim to convert each others’ point of view. Argumentative reinforcement learning (RL) agents can learn from experience via trial and error. For an agent learning to argue with other agents, it would be useful to discover some useful argument patterns i.e. state and action pairs, which will enable a RL agent to transfer what has been learned from one domain to another. A dialogue model is needed to manage the evolving dialogue between agents. In this paper we adopt the DE dialogue game and construct an RL agent that can win a debate with the minimum number of moves. The result is promising so far and this enables us to move forward to transfer learning in order to generalise our approach. It is anticipated that our research will contribute to the design and implementation of argumentative learning agents.