Symbolic decision theory and autonomous systems
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Artificial Intelligence - Special issue on knowledge representation
C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient top-down induction of logic programs
ACM SIGART Bulletin
Optimal composition of real-time systems
Artificial Intelligence
Bayesian Update of Recursive Agent Models
User Modeling and User-Adapted Interaction
Machine Learning
Rational Communicative Behavior in Anti-Air Defense
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Provably bounded-optimal agents
Journal of Artificial Intelligence Research
Deriving multi-agent coordination through filtering strategies
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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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.