Artificial Multiagent Learning: Papers from the AAAI Fall Symposium
Sean Luke, Chair
Multiagent systems is a subset of distributed artificial intelligence that emphasizes the joint behaviors of agents in environments with some degree of autonomy. In most such environments there are constraints placed on the degree to which any agent may know what other agents know, or on their communication capabilities, such that the system must have distributed control and cannot be solved with a master-slave model via a single master agent. In recent years there has been increasing interest in applying machine learning techniques to multiagent systems problems. The presence of large numbers of agents, increasingly complex agent behaviors, partially observable environments, and the mutual adaptation of agent behaviors make the learning process a challenging one. These problems are further complicated by noisy sensor data, local bandwidth-limited communication, unplanned faults in hardware agents, and stochastic environments. The goal of this symposium was to bring together researchers from diverse areas of the multiagent learning community. Research presented at the symposium will include learning topics such as coevolution, multiagent reinforcement learning, multi-robot issues, stochastic and repeated games, agent modeling, team formation, swarms, and distributed scheduling.