Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Improving Performance of Open Access Clinics

Open Access Scheduling has shown great promise in allowing health care practices to provide same-day access, and to match patients with their regular physicians. However, similarly to traditional clinics where appointments are pre-booked, open access clinics are also frustrated with long waits, long idle time and long overtime due to uncertainties such as patient no-shows, variable service time and variable daily demand. These aspects have not been studied previously in an open access setting. This study investigates different management options to improve clinical performance in terms of patient waiting time, doctor idle time and clinic overtime. Other factors studied with a simulation model include client load and placement of pre-booked slots. Results show that a proper panel size is critical to obtain good performance for open access clinics, and that good choices for management options depend on the client load.

Exploring Healthcare Experiences of Transgender Individuals

The purpose of this study was to explore how transgender individuals were supported to navigate the healthcare system to achieve positive healthcare experiences. A single case study was conducted in Southern Ontario, which included ten individual interviews. Data was analyzed through thematic analysis, allowing for seven themes to emerge within macro (large-scale system), meso (local/interpersonal), and micro (individual/internal) levels of healthcare system support. Themes that emerged within the levels of system support included: 1) existing deficits with hope for change; 2) significant external supports; 3) importance of informal networking; 4) support from local area family physicians and walk-in clinics; 5) navigating the healthcare system alone; 6) personality traits for successful healthcare experiences; and 7) the development of strategies to achieve positive healthcare experiences. This study outlined factors that contributed to positive healthcare experiences for transgender individuals, showing that meso and micro level support are compensating for large-scale healthcare system deficits.