ESCELL: Emergent Symbolic Cellular Language

TL;DR

ESCELL demonstrates how multiple agents can develop an emergent symbolic language to communicate about cells, achieving high accuracy in identifying cell phenotypes in a referential game setting.

Contribution

The paper introduces ESCELL, a novel method for emergent communication in multi-agent systems focused on cellular reasoning, with new game variants and high-performance results.

Findings

Achieved 93.2% accuracy in cell identification task.

Developed a successful emergent language for communication.

Introduced a new signaling game with image-based sender input.

Abstract

We present ESCELL, a method for developing an emergent symbolic language of communication between multiple agents reasoning about cells. We show how agents are able to cooperate and communicate successfully in the form of symbols similar to human language to accomplish a task in the form of a referential game (Lewis' signaling game). In one form of the game, a sender and a receiver observe a set of cells from 5 different cell phenotypes. The sender is told one cell is a target and is allowed to send one symbol to the receiver from a fixed arbitrary vocabulary size. The receiver relies on the information in the symbol to identify the target cell. We train the sender and receiver networks to develop an innate emergent language between themselves to accomplish this task. We observe that the networks are able to successfully identify cells from 5 different phenotypes with an accuracy of 93.2%. We also introduce a new form of the signaling game where the sender is shown one image instead of all the images that the receiver sees. The networks successfully develop an emergent language to get an identification accuracy of 77.8%.