Emergentism and pragmatics are two research fields that study the dynamics of linguistic communication along quite different timescales and intelligence levels. From the perspective of multi-agent reinforcement learning, they correspond to stochastic games with reinforcement training and stage games with opponent awareness, respectively. Given that their combination has been explored in linguistics, in this work, we combine computational models of short-term mutual reasoning-based pragmatics with long-term language emergentism. We explore this for agent communication in two settings, referential games and Starcraft II, assessing the relative merits of different kinds of mutual reasoning pragmatics models both empirically and theoretically. Our results shed light on their importance for making inroads towards getting more natural, accurate, robust, fine-grained, and succinct utterances.
Yipeng Kang (Tsinghua University)
Tonghan Wang (Tsinghua University)
Tonghan Wang is currently a Master student working with Prof. Chongjie Zhang at Institute for Interdisciplinary Information Sciences, Tsinghua University, headed by Prof. Andrew Yao. His primary research goal is to develop innovative models and methods to enable effective multi-agent cooperation, allowing a group of individuals to explore, communicate, and accomplish tasks of higher complexity. His research interests include multi-agent learning, reasoning under uncertainty, reinforcement learning, and representation learning in multi-agent systems.
Gerard de Melo (Hasso Plattner Institute)
Related Events (a corresponding poster, oral, or spotlight)
2020 Poster: Incorporating Pragmatic Reasoning Communication into Emergent Language »
Tue Dec 8th 05:00 -- 07:00 AM Room Poster Session 0