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- W2993021567 abstract "We propose a data-driven framework to enable the modeling and optimization of human-machine interaction processes, e.g., systems aimed at assisting humans in decision-making or learning, work-load allocation, and interactive advertising. This is a challenging problem for several reasons. First, humans’ behavior is hard to model or infer, as it may reflect biases, long term memory, and sensitivity to sequencing, i.e., transience and exponential complexity in the length of the interaction. Second, due to the interactive nature of such processes, the machine policy used to engage with human may bias possible data-driven inferences. Finally, in choosing machine policies that optimize interaction rewards, one must, on the one hand, avoid being overly sensitive to error/variability in the estimated human model, and on the other, being overly deterministic/predictable which may result in poor human ‘engagement’ in the interaction. To meet these challenges, we propose a robust approach, based on the maximum entropy principle, which iteratively estimates human behavior and optimizes the machine policy, namely Alternating Entropy-Reward Ascent (AREA) algorithm. We characterize AREA, in terms of its space and time complexity and convergence. We also provide an initial validation based on synthetic data generated by an established noisy nonlinear model for human decision-making." @default.
- W2993021567 created "2019-12-13" @default.
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- W2993021567 date "2019-09-01" @default.
- W2993021567 modified "2023-10-01" @default.
- W2993021567 title "Modeling and optimization of Human-Machine Interaction Processes via the Maximum Entropy Principle" @default.
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- W2993021567 doi "https://doi.org/10.1109/allerton.2019.8919959" @default.
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