Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract)
This work addresses the challenge of detecting complex social phenomena in online political strategy games, which is incremental as it builds on existing methods with a graph-aware reinforcement learning twist.
The paper tackled the problem of modeling persuasive strategies in multiparty discourse by developing a two-tier approach that encodes sociolinguistic behavior as linguistic features and uses reinforcement learning to estimate player advantages in Diplomacy games, showing robust performance on a dataset of over 15,000 messages from 78 users.
This abstract proposes an approach towards goal-oriented modeling of the detection and modeling complex social phenomena in multiparty discourse in an online political strategy game. We developed a two-tier approach that first encodes sociolinguistic behavior as linguistic features then use reinforcement learning to estimate the advantage afforded to any player. In the first tier, sociolinguistic behavior, such as Friendship and Reasoning, that speakers use to influence others are encoded as linguistic features to identify the persuasive strategies applied by each player in simultaneous two-party dialogues. In the second tier, a reinforcement learning approach is used to estimate a graph-aware reward function to quantify the advantage afforded to each player based on their standing in this multiparty setup. We apply this technique to the game Diplomacy, using a dataset comprising of over 15,000 messages exchanged between 78 users. Our graph-aware approach shows robust performance compared to a context-agnostic setup.