Revisiting Bounded-Suboptimal Safe Interval Path Planning

Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles. SIPP returns provably optimal solutions. However, in many practical applications of SIPP such as path planning for robots, one would like to trade-off optimality for shorter planning time. In this paper we explore different ways to build a bounded-suboptimal SIPP and discuss their pros and cons. We compare the different bounded-suboptimal versions of SIPP experimentally. While there is no universal winner, the results provide insights into when each method should be used

Supporting Post-disaster Recovery with Agent-based Modeling in Multilayer Socio-physical Networks

The examination of post-disaster recovery (PDR) in a socio-physical system enables us to elucidate the complex relationships between humans and infrastructures. Although existing studies have identified many patterns in the PDR process, they fall short of describing how individual recoveries contribute to the overall recovery of the system. To enhance the understanding of individual return behavior and the recovery of point-of-interests (POIs), we propose an agent-based model (ABM), called PostDisasterSim. We apply the model to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network comprising the human layer, the social infrastructure layer, and the physical infrastructure layer, using mobile phone location data and POI data. Based on prior studies and a household survey, we develop the ABM to simulate how evacuated individuals return to their homes, and social and physical infrastructures recover. By implementing the ABM, we unveil the heterogeneity in recovery dynamics in terms of agent types, housing types, household income levels, and geographical locations. Moreover, simulation results across nine scenarios quantitatively demonstrate the positive effects of social and physical infrastructure improvement plans. This study can assist disaster scientists in uncovering nuanced recovery patterns and policymakers in translating policies like resource allocation into practice.Comment: 28 pages, 10 figure

Discards & Diverse Economies: Reuse in Rural Maine

This dissertation presents an ethnographic exploration of diverse reuse economies in rural Maine in an effort to illuminate both how used goods move between people and organizations, as well as the value of that movement for people and communities. In response to a growing number of calls for research into the social dimensions of circular economies, this research explores the varied and uneven impacts of materials reuse as they are experienced by local participants. This work uses a qualitative approach, drawing on two main methods: participant observation in reuse establishments and in-depth, semi-structured interviews with reuse participants. This rich qualitative data provides a detailed picture of reuse activities at a local scale, and helps us understand the complex relationships formed and perpetuated through reuse. This research presents three important contributions to the literature on reuse and circular economies. First, there are strong associations between reuse practices (buying, selling, lending, and gifting used goods) and social capital. This suggests that reuse practices might contribute to the social fabric of communities, building trust, relationships, cooperation, and support. Yet my research also highlights the negative consequences of social capital, such as when people are excluded from networks, resources, and opportunities along racialized and classed divisions. My research emphasizes that both reuse and social capital must be understood as complex practices that have the potential to exclude as well as include. Policymakers and community members eager to contribute to localized wellbeing must understand and plan for these complex effects as they create supports for localized reuse. Secondly, this research illustrates key differences between localized reuse economies and globalized platforms for exchange. The social value offered by reuse economies is absent in online, frictionless exchanges that allow for goods to move quickly between buyers and sellers. I find that the friction – the slowness, awkwardness, and time-intensiveness – of localized reuse is what offers potential social benefits. Growing globalized reuse exchanges forecloses important opportunities to foster these important social networks. Finally, my work examines the labor that powers localized reuse economies. I find that the unwaged, voluntary labor of elderly volunteers is often unseen and unvalued. Indeed, volunteers are performing emotional and affective labor as they manage the surplus of their communities. This research suggests that policies designed to address material surplus do so with these laborers in mind. Taken together this dissertation envisions localized reuse economies as diverse economies defined by complexity and social relationships. These findings offer policymakers and local decision-makers solutions for promoting just and equitable localized circular economies

JUNE-Germany: An Agent-Based Epidemiology Simulation including Multiple Virus Strains, Vaccinations and Testing Campaigns

The June software package is an open-source framework for the detailed simulation of epidemics based on social interactions in a virtual population reflecting age, gender, ethnicity, and socio-economic indicators in England. In this paper, we present a new version of the framework specifically adapted for Germany, which allows the simulation of the entire German population using publicly available information on households, schools, universities, workplaces, and mobility data for Germany. Moreover, JuneGermany incorporates testing and vaccination strategies within the population as well as the simultaneous handling of several different virus strains. First validation tests of the framework have been performed for the state of Rhineland Palatinate based on data collected between October 2020 and December 2020 and then extrapolated to March 2021, i.e. the end of the second wave.Comment: 10 pages, 11 figure

Making in the Midst of Pandemic: The Impact of the COVID-19 Pandemic on Two Public Library Makerspaces

This qualitative research explored the impact of the COVID-19 pandemic on makerspaces in the United States which were subject to public health guidelines and challenged with limited/no access to facilities. This multi-case study examined two public library makerspaces, and addressed these research questions: (1) How did the pandemic affect makerspace operations and access, and the teaching and learning that occurs there? (2) How did makerspace leaders respond to the challenges of the pandemic? (3) How did makerspaces evolve during the COVID-19 pandemic? I developed the Conceptual Framework for Studying the Impact of Pandemic on Public Library Makerspaces which informed the research questions and functioned as template for the research. I collected data digitally and used qualitative coding for within- and cross-case analysis. Findings indicated that the makerspaces shifted from a physical to a virtual setting using community of practice elements. Makerspace staff responded to challenges by reallocating or seeking alternate funding, embracing virtual opportunities to engage patrons in events and instruction, implementing online scheduling calendars, and restructuring services to offer maximum events/access. The makerspaces evolved in terms of staffing, funding, operations, equipment, and offerings. Findings support makerspaces as communities of practice. The study informs makerspace professionals who are adapting to change

The tidal evolution of dark galactic substructures

In this thesis we study the tidal evolution of dark matter subhaloes. We first look at reproducing the work of van den Bosch and Ogiya (2018), who suggested that modern cosmological simulations still suffer from excessive disruption of subhaloes due to gravitational tides. We were able to reproduce the results qualitatively, but not exactly, and found that our subhaloes were slightly more robust and resistant to disruption. We examine substructures in state-of-the-art cosmological simulations. We develop a technique to study substructures of a Milky Way-like halo from the Aquarius project (Springel et al., 2008) using the HEX technique (Lowing et al., 2011). HEX allows us to realistically model the potential of a halo in a computationally efficient fashion, which means that it is possible to run a large number of simulations of individual subhaloes. We find that the softening length does not seem to have a significant effect on the survival of substructure in realistic conditions. We find that with sufficient resolution, subhaloes which were lost in the original simulation do survive until the end. However, these subhaloes are relatively rare. We thus confirm that while there is artificial disruption, this does not appear to affect the substructure population as a whole in a realistic simulation

Goal reasoning for autonomous agents using automated planning

Mención Internacional en el título de doctorAutomated planning deals with the task of finding a sequence of actions, namely a plan, which achieves a goal from a given initial state. Most planning research consider goals are provided by a external user, and agents just have to find a plan to achieve them. However, there exist many real world domains where agents should not only reason about their actions but also about their goals, generating new ones or changing them according to the perceived environment. In this thesis we aim at broadening the goal reasoning capabilities of planningbased agents, both when acting in isolation and when operating in the same environment as other agents. In single-agent settings, we firstly explore a special type of planning tasks where we aim at discovering states that fulfill certain cost-based requirements with respect to a given set of goals. By computing these states, agents are able to solve interesting tasks such as find escape plans that move agents in to safe places, hide their true goal to a potential observer, or anticipate dynamically arriving goals. We also show how learning the environment’s dynamics may help agents to solve some of these tasks. Experimental results show that these states can be quickly found in practice, making agents able to solve new planning tasks and helping them in solving some existing ones. In multi-agent settings, we study the automated generation of goals based on other agents’ behavior. We focus on competitive scenarios, where we are interested in computing counterplans that prevent opponents from achieving their goals. We frame these tasks as counterplanning, providing theoretical properties of the counterplans that solve them. We also show how agents can benefit from computing some of the states we propose in the single-agent setting to anticipate their opponent’s movements, thus increasing the odds of blocking them. Experimental results show how counterplans can be found in different environments ranging from competitive planning domains to real-time strategy games.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: Eva Onaindía de la Rivaherrera.- Secretario: Ángel García Olaya.- Vocal: Mark Robert

강화학습을 이용한 공항 임시폐쇄 상황에서의 항공 일정계획 복원

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 문일경.An airline scheduler plans flight schedules with efficient resource utilization. However, unpredictable events, such as the temporary closure of an airport, disrupt planned flight schedules. Therefore, recovering disrupted flight schedules is essential for airlines. We propose Q-learning and Double Q-learning algorithms using reinforcement learning approach for the aircraft recovery problem (ARP) in cases of temporary closures of airports. We use two recovery options: delaying departures of flights and swapping aircraft. We present an artificial environment of daily flight schedules and the Markov decision process (MDP) for the ARP. We evaluate the proposed approach on a set of experiments carried out on a real-world case of a Korean domestic airline. Computational experiments show that reinforcement learning algorithms recover disrupted flight schedules effectively, and that our approaches flexibly adapt to various objectives and realistic conditions.항공사는 보유하고 있는 자원을 최대한 효율적으로 사용하여 항공 일정계획을 수립하기 위해 비용과 시간을 많이 소모하게 된다. 하지만 공항 임시폐쇄와 같은 긴급 상황이 발생하면 항공편의 비정상 운항이 발생하게 된다. 따라서 이러한 상황이 발생하였을 때, 피해를 최대한 줄이기 위해 항공 일정계획을 복원하게 된다. 본 연구는 강화학습을 이용하여 공항 임시폐쇄 상황에서 항공 일정계획 복원 문제를 푼다. 본 연구에서는 항공기 복원 방법으로 항공편 지연과 항공기 교체의 두 가지 방법을 채택하였으며, 항공 일정계획 복원 문제에 강화학습을 적용하기 위해서 마르코프 결정 과정과 강화학습 환경을 구축하였다. 본 실험을 위해 대한민국 항공사의 실제 국내선 항공 일정계획을 사용하였다. 강화학습 알고리즘을 사용하여 기존의 연구에 비해 항공 일정계획을 효율적으로 복원하였으며, 여러 현실적인 조건과 다양한 목적함수에 유연하게 적용하였다.Abstract i Contents iv List of Tables v List of Figures vi Chapter 1 Introduction 1 Chapter 2 Literature Review 7 Chapter 3 Problem statement 11 3.1 Characteristics of aircraft, flights, and flight schedule requirements 11 3.2 Definitions of disruptions and recovery options and objectives of the problem 13 3.3 Assumptions 16 3.4 Mathematical formulations 19 Chapter 4 Reinforcement learning for aircraft recovery 24 4.1 Principles of reinforcement learning 24 4.2 Environment 27 4.3 Markov decision process 29 Chapter 5 Reinforcement learning algorithms 33 5.1 Q-learning algorithm 33 5.2 Overestimation bias and Double Q-learning algorithm 36 Chapter 6 Computational experiments 38 6.1 Comparison between reinforcement learning and existing algorithms 39 6.2 Performance of the TLN varying the size of delay arcs 46 6.3 Aircraft recovery for a complex real-world case: a Korean domestic airline 48 6.4 Validation for different objectives 54 6.5 Managerial insights 57 Chapter 7 Conclusions 59 Bibliography 61 국문초록 69Maste