Markov Games for Persuasive Dialogue
Niklas Rach, Wolfgang Minker, Stefan Ultes · Frontiers in artificial intelligence and applications · 2018
This work discusses the formulation of argumentative dialogue as Markov game. We show how formal systems for persuasive dialogues that adhere to a certain structure can be reformulated as Markov games and thus be addressed as Reinforcement Learning task in a multi-agent setting. We validate our approach on an implementation of a proof of principle scenario where we show that the optimal policy can be learned.