Reasoning about Benefits and Costs of Interaction with Users in Real-time Decision Making Environments with Application to Healthcare Scenarios
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This thesis examines the problem of having an intelligent agent reasoning about interaction with users in real-time decision making environments. Our work is motivated by the models of Fleming and Cheng, which reason about interaction sensitive to both expected quality of decision (following interaction) and cost of bothering users. In particular, we are interested in dynamic, time critical scenarios. This leads first of all to a novel process known as strategy regeneration, whereby the parameter values representing the users and the task at hand are refreshed periodically, in order to make effective decisions about which users to interact with, for the best decision making. We also introduce two new parameters that are modeled: each user's lack of expertise (with the task at hand) and the level of criticality of each task. These factors are then integrated into the process of reasoning about interaction to choose the best overall strategy, deciding which users to ask to resolve the current task. We illustrate the value of our framework for the application of decision making in hospital emergency room scenarios and offer validation of the approach, both through examples and from simulations. To sum up, we provide a framework for reasoning about interaction with users through user modeling for dynamic environments. In addition, we present some insights into how to improve the process of hospital emergency room decision making.