ECAgents: Embodied and Communicating Agents

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About ECAgents

ECAgents is a transdisciplinary European research project. Its goal is to provide a better understanding of the role of communication in collections of embodied and situated agents (real and simulated robots). This project involves people from different fields such as computer science, robotics, biology, physics and mathematics.

Our contribution to the ECAgent project

Our work focuses on the prerequisites for communication, which need to be in place before embodied agents can start bootstraping communication systems of any complexity. These prerequisites depend on the complexity of the agents, the complexity of the environment, and the complexity of the agent's set tasks.

We use artificial evolution to search for the emergence of communication and neural networks as the underlying agent control mechanism.

Specifically, we are exploring the following prerequisites :

  • The dynamic of the environment. For instance, an agent should look for food, but food placement as well as food caracteritics, such as colour, are dynamic. Some potential food sources in the world are "good", but others are "bad". The agents need to explore and report their discovery to other agents in order to maximise the group fitness.
  • The genetic relatedness and the level of selection. Does a homogenous group perform better then a heterogenous one? Is it better to do individual or group selection? Can we find a general principle out of different experiments involving different tasks and environment dynamics? This work is done in collaboration with the EvoAnts projects project.
  • The neural network architecture. To which point can we go without a hidden layer? Is it necessary to balance sensor modality weight (preprocess vision because of its several pixels)? Is memory (recurent neurons) mandatory? And if yes, how many, with which connections?
  • The communication medium structure. Is one medium sufficient (like vision)? Is it necessary to have differents channels of differents properties (like sound and vision)? Is local communication mandatory?

Our agents are S-bots, which were created as part of the Swarmbot project. Both simulated and real one are used.


We are exploring and analysing the bootstraping of communication with several starting conditions, neural architectures and evolutionary conditions using virtual s-bots in Enki.

We are porting some of our experiments to the real s-bot robots.

We are also exploring what are the mechanisms to provide a smooth path for evolution of signaling.

To conduct our experiments, we have developed a fast 2D physics-based simulator and an evolutionary framework. Both are open-source.

Virtual s-bots going to food sources. AVI, (4.2 MB).

Virtual s-bots looking for food and avoiding poison. The color of the food and the poison is randomly assigned. AVI, (20.0 MB).

Real s-bots looking for food and avoiding poison. AVI, (13.5 MB).