Sophisticated agents operating in open environments must make complex real-time control decisions on scheduling and coordination of domain activities. These decisions are made in the context of limited resources and uncertainty about the outcomes of activities. Many efficient architectures and algorithms that support these computation-intensive activities have been developed and studied. However, none of these architectures explicitly reason about the consumption of time and other resources by these activities, which may degrade an agent's performance. The problem of sequencing execution and computational activities without consuming too many resources in the process, is the meta-level control problem for a resource-bounded rational agent.; The focus of this research is to provide effective allocation of computation and unproved performance of individual agents in a cooperative multi-agent system. This is done by approximating the ideal solution to meta-level decisions made by these agents using reinforcement learning methods. A meta-level agent control architecture for meta-level reasoning with bounded computational overhead is described. This architecture supports decisions on when to accept, delay or reject a new task, when it is appropriate to negotiate with another agent, whether to renegotiate when a negotiation task fails, how much effort to put into scheduling when reasoning about a new task and whether to reschedule when actual execution performance deviates from expected performance. The major contributions of this work are: a resource-bounded framework that supports of the agent state which is used by hand-generated heuristic strategies to make meta-level control decisions; and a reinforcement learning based approach which automatically learns efficient meta-level control policies.
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