The Multi-Agent Programming Contest: A r\'esum\'e

The Multi-Agent Programming Contest, MAPC, is an annual event organized since 2005 out of Clausthal University of Technology. Its aim is to investigate the potential of using decentralized, autonomously acting intelligent agents, by providing a complex scenario to be solved in a competitive environment. For this we need suitable benchmarks where agent-based systems can shine. We present previous editions of the contest and also its current scenario and results from its use in the 2019 MAPC with a special focus on its suitability. We conclude with lessons learned over the years.Comment: Submitted to the proceedings of the Multi-Agent Programming Contest 2019, to appear in Springer Lect. Notes Computer Challenges Series https://www.springer.com/series/1652

GOAL-DTU: Development of Distributed Intelligence for the Multi-Agent Programming Contest

We provide a brief description of the GOAL-DTU system for the agent contest, including the overall strategy and how the system is designed to apply this strategy. Our agents are implemented using the GOAL programming language. We evaluate the performance of our agents for the contest, and finally also discuss how to improve the system based on analysis of its strengths and weaknesses.Comment: 28 pages, 45 figure

Empowerment and State-dependent Noise : An Intrinsic Motivation for Avoiding Unpredictable Agents

Empowerment is a recently introduced intrinsic motivation algorithm based on the embodiment of an agent and the dynamics of the world the agent is situated in. Computed as the channel capacity from an agent’s actuators to an agent’s sensors, it offers a quantitative measure of how much an agent is in control of the world it can perceive. In this paper, we expand the approximation of empowerment as a Gaussian linear channel to compute empowerment based on the covariance matrix between actuators and sensors, incorporating state dependent noise. This allows for the first time the study of continuous systems with several agents. We found that if the behaviour of another agent cannot be predicted accurately, then interacting with that agent will decrease the empowerment of the original agent. This leads to behaviour realizing collision avoidance with other agents, purely from maximising an agent’s empowermentFinal Accepted Versio

Bang: a system for training and visualization in multi-agent team formation

In this demo participants will explore Bang, a system for multiagent team formation. Bang automatically selects exercises for training agents, and allows an operator to visualize the expected performance of possible teams, guiding in the agent selection process. Bang is used in the context of programming competitions, a real-world challenge that involves human teams, and significantly improved the performance of the teams of CEFET-MG University