Multiagent Learning Systems and Expert Agents

Claudia V. Goldman, Jeffrey S. Rosenschein

This paper focuses on two main research topics we axe investigating. First, we investigate how agents can learn strategic behavior in a teacher-learner model. The notion of the teacher here should be understood as a "trainer". We present the general teacher-learner model together with results from experiments per-formed in the traffic hghts domain. Second, we investigate how agents can learn to be-come experts, and eventually organize themselves ap-propriately for a range of tasks. The model is based on evolutionary processes that lead to organizations of experts. In our case, the organization emerges as a step prior to the execution of a task, and as a general process related to a range of problems in a domain. To explore these ideas, we designed and implemented a testbed based on the idea of the game of Life.

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