Reasoning about Benefits and Costs of Interaction with Users in Real-time Decision Making Environments with Application to Healthcare Scenarios

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

An Ex-Ante Rational Distributed Resource Allocation System using Transfer of Control Strategies for Preemption with Applications to Emergency Medicine

Within the artificial intelligence subfield of multiagent systems, one challenge that arises is determining how to efficiently allocate resources to all agents in a way that maximizes the overall expected utility. In this thesis, we explore a distributed solution to this problem, one in which the agents work together to coordinate their requests for resources and which is considered to be ex-ante rational: in other words, requiring agents to be willing to give up their current resources to those with greater need by reasoning about what is for the common good. Central to our solution is allowing for preemption of tasks that are currently occupying resources; this is achieved by introducing a concept from adjustable autonomy multiagent systems known as a transfer of control (TOC) strategy. In essence a TOC strategy is a plan of an agent to acquire resources at future times, and can be used as a contingency plan that an agent will execute if it loses its current resource. The inclusion of TOC strategies ultimately provides for a greater optimism among agents about their future resource acquisitions, allowing for more generous behaviours, and for agents to more frequently agree to relinquish current resources, resulting in more effective preemption policies. Three central contributions arise. The first is an improved methodology for generating transfer of control strategies efficiently, using a dynamic programming approach, which enables a more effective employment of TOCs in our resource allocation solution. The second is an important clarification of the value of integrating learning techniques in order for agents to acquire improved estimates of the costs of preemption. The last is a validation of the overall multiagent resource allocation (MARA) solution, using simulations which show quantifiable benefits of our novel approach. In particular, we consider in detail the emergency medical application of mass casualty incidents and are able to demonstrate that our approach of integrating transfer of control strategies results in effective allocation of patients to doctors: ones which in simulations re- sult in dramatically fewer patients in a critical healthstate than are produced by competing MARA algorithms. In short, we offer a principled solution to the problem of preemption, allowing the elimination of a source of inefficiencies in fully distributed multiagent resource allocation systems; a faster method for generation of transfer of control strategies; and a convincing application of the system to a real world problem where human lives are at stake