Papers from the AAAI Workshop
Eduardo Alonso, Michael Bowling, Enric Plaza, and Peter Stone,Cochairs
When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behavior optimally in advance. Agents therefore have to learn from and adapt to their environment. This task is even more complex when nature is not the only source of uncertainty, and the agent is situated in an environment that contains other agents with potentially different capabilities, goals, and beliefs. Multiagent learning, that is, the ability of the agents to learn how to cooperate and compete, becomes crucial in such domains.
The goal of this workshop was to increase awareness and interest in adaptive agent research, encourage collaboration between ML experts and agent system experts, and give a representative overview of current research in the area of adaptive agents. The workshop served as an inclusive forum for the discussion on ongoing or completed work in both theoretical and practical issues.