# Miss Tools and Mr Fruit: Emergent communication in agents learning about object affordances

arXiv: https://arxiv.org/abs/1905.11871 · HTML: https://mlsanity.com/paper/1905.11871/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 11 of 11)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [The Emergence of the Shape Bias Results from Communicative Efficiency](https://mlsanity.com/paper/2109.06232/) (2021)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [Dynamic population-based meta-learning for multi-agent communication with natural language](https://mlsanity.com/paper/2110.14241/) (2021)
- [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026)
- [Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study](https://mlsanity.com/paper/2305.19936/) (2023)
- [Over-communicate no more: Situated RL agents learn concise communication protocols](https://mlsanity.com/paper/2211.01480/) (2022)
- [PatchGame: Learning to Signal Mid-level Patches in Referential Games](https://mlsanity.com/paper/2111.01785/) (2021)
- [Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2307.05004/) (2023)
- [A Combinatorial Approach to Neural Emergent Communication](https://mlsanity.com/paper/2410.18806/) (2024)

## Abstract (arXiv)

Recent research studies communication emergence in communities of deep network agents assigned a joint task, hoping to gain insights on human language evolution. We propose here a new task capturing crucial aspects of the human environment, such as natural object affordances, and of human conversation, such as full symmetry among the participants. By conducting a thorough pragmatic and semantic analysis of the emergent protocol, we show that the agents solve the shared task through genuine bilateral, referential communication. However, the agents develop multiple idiolects, which makes us conclude that full symmetry is not a sufficient condition for a common language to emerge.