Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play
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Abstract (arXiv)
In this paper, we propose and consider the problem of cooperative language acquisition as a particular form of the ad hoc team play problem. We then present a probabilistic model for inferring a speaker's intentions and a listener's semantics from observing communications between a team of language-users. This model builds on the assumptions that speakers are engaged in positive signalling and listeners are exhibiting positive listening, which is to say the messages convey hidden information from the listener, that then causes them to change their behaviour. Further, it accounts for potential sub-optimality in the speaker's ability to convey the right information (according to the given task). Finally, we discuss further work for testing and developing this framework.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 20 May 2023.
Builds on
7 of 20 references.
References · 7
- 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 406
- 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
- 2016Multi-Agent Cooperation and the Emergence of (Natural) Languagecited by 185
- 2020Reward-rational (implicit) choice: A unifying formalism for reward learningcited by 120
- 2019On the Pitfalls of Measuring Emergent Communicationcited by 71
- 2017Emergent Translation in Multi-Agent Communicationcited by 22
- 2020Learning to cooperate: Emergent communication in multi-agent navigationcited by 18
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.