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This abstract proposes an approach towards goal-oriented modeling of thedetection and modeling complex social phenomena in multiparty discourse in anonline political strategy game. We developed a two-tier approach that firstencodes sociolinguistic behavior as linguistic features then use reinforcementlearning to estimate the advantage afforded to any player. In the first tier,sociolinguistic behavior, such as Friendship and Reasoning, that speakers useto influence others are encoded as linguistic features to identify thepersuasive strategies applied by each player in simultaneous two-partydialogues. In the second tier, a reinforcement learning approach is used toestimate a graph-aware reward function to quantify the advantage afforded toeach player based on their standing in this multiparty setup. We apply thistechnique to the game Diplomacy, using a dataset comprising of over 15,000messages exchanged between 78 users. Our graph-aware approach shows robustperformance compared to a context-agnostic setup.

Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract)