The communication paradigm facilitates team coordination while being robust to lost messages and active interference from opponents. Second, the thesis introduces layered learning, a general-purpose machine learning paradigm for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable. Given a hierarchical task decomposition, layered learning allows for learning at each level of the hierarchy, with learning at each level directly affecting learning at the next higher level. TPOT-RL is designed for domains in which agents cannot necessarily observe the state changes when other team members act. Detailed algorithmic descriptions of the agents' behaviors as well as their source code are included in the thesis.
Empirical results validate all four contributions within the simulated robotic soccer domain.
The generality of the contributions is verified by applying them to the real robotic soccer, and network routing domains. Ultimately, this dissertation demonstrates that by learning portions of their cognitive processes, selectively communicating, and coordinating their behaviors via common knowledge, a group of independent agents can work towards a common goal in a complex, real-time, noisy, collaborative, and adversarial environment. All files below are gzipped Postscript and formatted for double-sided printing. Uncompressed Postscript and PDF versions are available from the CS tech reports page . CMUnited-97 source code .