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Recent work has defined an optimal reward problem (ORP) in which an agent designer, with an objective reward function that evaluates an agent's behavior, has a choice of what reward function to build into a learning or planning agent to guide its behavior. Existing results on ORP show weak miti-gation of limited computational resources, i.e., the existence(More)
Computational experiments have been used extensively to study language emergence by simulating the evolution of language over generations of interacting agents. Much of this work has focused on understanding the mechanisms of how language might have evolved. We propose a complementary approach helpful in understanding why specific properties of language(More)
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