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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)
— Principal Components Analysis (PCA) is often used to project high-dimensional signals to lower dimensional subspaces defined by basis vectors that maximize the variance of the projected signals. Data containing variations of relatively short duration and small magnitude, such as those seen in EEG signals, may not be captured by PCA when applied to time(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)
This paper is dedicated to the " living cognition " issues, which concern the ability of a cognitive model to simulate humans' mental activities when dynamically interacting with the external environment. After having introduced the theoretical foundations of this approach, an integrative COgnitive Simulation MOdel of the DRIVEr is presented (i.e.(More)
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