  We propose Searching with Opponent-Awareness (SOA), an approach to leverage
opponent-aware planning without explicit or a priori opponent models for
improving performance and social welfare in multi-agent systems. To this end,
we develop an opponent-aware MCTS scheme using multi-armed bandits based on
Learning with Opponent-Learning Awareness (LOLA) and compare its effectiveness
with other bandits, including UCB1. Our evaluations include several different
settings and show the benefits of SOA are especially evident with increasing
number of agents.
