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

Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2014 Annual Conference:Human Factors in high reliability industries

On the occasion of the 2014 Meeting in Lisbon, Portuga

Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2014 Annual Conference:Human Factors in high reliability industries

On the occasion of the 2014 Meeting in Lisbon, Portuga

Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2014 Annual Conference:Human Factors in high reliability industries

On the occasion of the 2014 Meeting in Lisbon, Portuga

A Framework for Resource Allocation in Time Critical Dynamic Environments Based on Social Welfare and Local Search and its Application to Healthcare

This thesis provides an artificial intelligence approach for the problem of resource allocation in time-critical dynamic environments. Motivated by healthcare scenarios such as mass casualty incidents, we are concerned with making effective decisions about allocating to patients the limited resources of ambulances, doctors and other medical staff members, in real-time, under changing circumstances. We cover two distinct stages: the Ambulance stage (at the location of the incident) and the Hospital stage (where the patient requires treatment). Our work addresses both determining the best allocation and supporting decision making (for medical staff to explore possible options). Our approach uses local search with social welfare functions in order to find the best allocations, making use of a centralized tracking of patients and resources. We also clarify how sensing can assist in updating the central system with new information. A key concept in our solution is that of a policy that attempts to minimize cost and maximize utility. To confirm the value of our approach, we present a series of detailed simulations of ambulance and hospital scenarios, and compare algorithms with competing principles of allocation (e.g. sickest first) and societal preferences (e.g. egalitarian allotment). In all, we offer a novel direction for resource allocation that is principled and that offers quantifiable feedback for professionals who are engaged in making resource allocation decisions

Pervasive computing reference architecture from a software engineering perspective (PervCompRA-SE)

Pervasive computing (PervComp) is one of the most challenging research topics nowadays. Its complexity exceeds the outdated main frame and client-server computation models. Its systems are highly volatile, mobile, and resource-limited ones that stream a lot of data from different sensors. In spite of these challenges, it entails, by default, a lengthy list of desired quality features like context sensitivity, adaptable behavior, concurrency, service omnipresence, and invisibility. Fortunately, the device manufacturers improved the enabling technology, such as sensors, network bandwidth, and batteries to pave the road for pervasive systems with high capabilities. On the other hand, this domain area has gained an enormous amount of attention from researchers ever since it was first introduced in the early 90s of the last century. Yet, they are still classified as visionary systems that are expected to be woven into people’s daily lives. At present, PervComp systems still have no unified architecture, have limited scope of context-sensitivity and adaptability, and many essential quality features are insufficiently addressed in PervComp architectures. The reference architecture (RA) that we called (PervCompRA-SE) in this research, provides solutions for these problems by providing a comprehensive and innovative pair of business and technical architectural reference models. Both models were based on deep analytical activities and were evaluated using different qualitative and quantitative methods. In this thesis we surveyed a wide range of research projects in PervComp in various subdomain areas to specify our methodological approach and identify the quality features in the PervComp domain that are most commonly found in these areas. It presented a novice approach that utilizes theories from sociology, psychology, and process engineering. The thesis analyzed the business and architectural problems in two separate chapters covering the business reference architecture (BRA) and the technical reference architecture (TRA). The solutions for these problems were introduced also in the BRA and TRA chapters. We devised an associated comprehensive ontology with semantic meanings and measurement scales. Both the BRA and TRA were validated throughout the course of research work and evaluated as whole using traceability, benchmark, survey, and simulation methods. The thesis introduces a new reference architecture in the PervComp domain which was developed using a novel requirements engineering method. It also introduces a novel statistical method for tradeoff analysis and conflict resolution between the requirements. The adaptation of the activity theory, human perception theory and process re-engineering methods to develop the BRA and the TRA proved to be very successful. Our approach to reuse the ontological dictionary to monitor the system performance was also innovative. Finally, the thesis evaluation methods represent a role model for researchers on how to use both qualitative and quantitative methods to evaluate a reference architecture. Our results show that the requirements engineering process along with the trade-off analysis were very important to deliver the PervCompRA-SE. We discovered that the invisibility feature, which was one of the envisioned quality features for the PervComp, is demolished and that the qualitative evaluation methods were just as important as the quantitative evaluation methods in order to recognize the overall quality of the RA by machines as well as by human beings