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Parameter Bias from Unobserved Effects in the Multinomial Logit Model of Consumer Choice
Over the past two decades, validation of choice models has focused on predictive validity rather than parameter bias. In real-world validation of choice models, true parameter values are unknown, soExpand
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An Experimental Investigation of the Impact of Information on Competitive Decision Making
TLDR
We investigate whether access to a decision aid and historical information of competitors' market outcomes yields more- or less-competitive decisions and outcomes in oligopolistic settings. Expand
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General Aviation Landing Flare Instructions
The present paper discusses the ability to determine low altitudes and challenges the effectiveness of current general aviation landing flare instrucfions. Conclusions are based on literary reviewExpand
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A Reinforcement Learning Model for Robots as Teachers*
TLDR
We present a reinforcement learning model influenced by human cognition which is repurposed to enhance human learning, investigate a robot's ability to encourage and motivate humans and improve their performance. Expand
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Learning Task-Based Instructional Policy for Excavator-Like Robots
We explore beyond existing work in learning from demonstration by asking the question: “Can robots learn to guide?”, that is, can a robot autonomously learn an instructional policy from expertExpand
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Models of health plan choice
TLDR
We compare and contrast approaches used by health economists and marketing researchers to explicitly allow for consumer heterogeneity in econometric models of health plan choice. Expand
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Using Human Reinforcement Learning Models to Improve Robots as Teachers
TLDR
We developed a reinforcement learning model for robotic teaching where the robot both attempts to learn an action sequence that leads to high reward (understood as successful human learning) and represents a human’s own learning process as a reinforcement process as well. Expand
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Semantic structure for robotic teaching and learning
TLDR
We enable a humanoid Baxter robot to build a semantically accessible framework for task learning, teaching and representation via active learning with human experts using hierarchical semantic labels. Expand
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Mutual Reinforcement Learning with Robot Trainers
TLDR
The researchers in this study have developed a novel approach using mutual reinforcement learning (MRL) where both the robot and human act as empathetic individuals who function as reinforcement learning agents for each other to achieve a particular task. Expand
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Can Co-robots Learn to Teach?
We explore beyond existing work on learning from demonstration by asking the question: Can robots learn to teach?, that is, can a robot autonomously learn an instructional policy from expertExpand
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