Flexible multi-agent decision making under time pressure

Autonomous agents need considerable computational resources to perform rational decision making. These demands are even more severe when other agents are present in the environment. In these settings, the quality of an agent's alternative behaviors depends not only on the state of the environment, but also on the actions of other agents, which in turn depend on the others' beliefs about the world, their preferences, and further on the other agents' beliefs about others, and so on. The complexity becomes prohibitive when large number of agents are present and when decisions have to be made under time pressure. In this paper, we investigate strategies intended to tame the computational burden by using offline computation in conjunction with online reasoning. We investigate two approaches. First, we use rules compiled offline to constrain alternative actions considered during online reasoning. This method minimizes overhead, but is not sensitive to changes in real-time demands of the situation at hand. Second, we use performance profiles computed offline and the notion of urgency (i.e., the value of time) computed online to choose the amount of information to be included during online deliberation. This method can adjust to various levels of real-time demands, but incurs some overhead associated with iterative deepening. We test our framework with experiments in a simulated anti-air defense domain. The experiments show that both procedures are effective in reducing computation time while offering good performance under time pressure.