Towards flexible multi-agent decision-making under time pressure

To perform rational decision-making, autonomous agents need considerable computational resources. In multi-agent settings, when other agents are present in the environment, these demands are even more severe. We investigate ways in which the agent's knowledge and the results of deliberative decision-making can be compiled to reduce the complexity of decision-making procedures and to save time in urgent situations. We use machine learning algorithms to compile decision-theoretic deliberations into condition-action rules on how to coordinate in a multi-agent environment. Using different learning algorithms, we endow a resource-bounded agent with a tapestry of decision making tools, ranging from purely reactive to fully deliberative ones. The agent can then select a method depending on the time constraints of the particular situation. We also propose combining the decision-making tools, so that, for example, more reactive methods serve as a pre-processing stage to the more accurate but slower deliberative decision-making ones. We validate our framework with experimental results in simulated coordinated defense. The experiments show that compiling the results of decision-making saves deliberation time while offering good performance in our multi-agent domain.