Coaching a simulated soccer team by opponent model recognition

摘要

In multiagent domains with adversarial and cooperative agents, team agents should be adaptive to the current environment and opponent. We introduce an online method to provide the agents with team plans that a “coach” agent generates in response to the specific opponents. The coach agent is equipped with a number of pre-defined opponent models. The coach is then able to quickly select between different models online by using a naive Bayes style algorithm, making the planning adaptive to the current adversary. The coach uses a Simple Temporal Network to represent team plans as coordinated movements among the multiple agents and it searches for an opponent-dependent plan for its teammates. This plan is then communicated to the agents, who execute the plan in a distributed fashion. The system is fully implemented in a simulated robotic soccer domain.