ESCELL: Emergent Symbolic Cellular Language
This addresses the challenge of emergent communication in multi-agent systems for tasks like cell phenotype identification, though it is incremental as it builds on existing signaling game frameworks.
The paper tackles the problem of enabling multiple agents to develop an emergent symbolic language for communication in a referential game involving cell identification, achieving accuracies of 93.2% in a standard setup and 77.8% in a variant where the sender sees only one image.
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%.