Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés

Energy efficient path planning: the effectiveness of Q-learning algorithm in saving energy

Includes bibliographical references.In this thesis the author investigated the use of a Q-learning based path planning algorithm to investigate how effective it is in saving energy. It is important to pursue any means to save energy in this day and age, due to the excessive exploitation of natural resources and in order to prevent drops in production in industrial environments where less downtime is necessary or other applications where a mobile robot running out of energy can be costly or even disastrous, such as search and rescue operations or dangerous environment navigation. The study was undertaken by implementing a Q-learning based path planning algorithm in several unstructured and unknown environments. A cell decomposition method was used to generate the search space representation of the environments, within which the algorithm operated. The results show that the Q-learning path planner paths on average consumed 3.04% less energy than the A* path planning algorithm, in a square 20% obstacle density environment. The Q-learning path planner consumed on average 5.79% more energy than the least energy paths for the same environment. In the case of rectangular environments, the Q-learning path planning algorithm uses 1.68% less energy, than the A* path algorithm and 3.26 % more energy than the least energy paths. The implication of this study is to highlight the need for the use of learning algorithm in attempting to solve problems whose existing solutions are not learning based, in order to obtain better solutions

Influence map-based pathfinding algorithms in video games

Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems by intelligent agents, ranging from computer games and applications to robotics. Pathfinding is a particular kind of search, in which the objective is to find a path between two nodes. A node is a point in space where an intelligent agent can travel. Moving agents in physical or virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able to navigate through its surrounding environment without avoiding obstacles, it does not seem intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games. Pathfinding algorithms work well with single agents navigating through an environment. In realtime strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large and dynamic game environments. On the contrary, influence maps are not used in pathfinding. Influence maps are a spatial reasoning technique that helps bots and players to take decisions about the course of the game. Influence map represent game information, e.g., events and faction power distribution, and is ultimately used to provide game agents knowledge to take strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g., capture an enemy location and win the game. Tactical decisions are based on small and precise actions, e.g., where to install a turret, where to hide from the enemy. This dissertation work focuses on a novel path search method, that combines the state-of-theart pathfinding algorithms with influence maps in order to achieve better time performance and less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho mais curto, desde a àrea jogos de computador até à robótica. Pathfinding é um tipo particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar. Agentes móveis em mundos físicos e virtuais são uma componente chave para a simulação de comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz parte das tarefas fundamentais de inteligencia artificial em vídeo jogos. Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados no pathfinding. Influence maps são uma técnica de raciocínio espacial que ajudam agentes inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo. Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding com influence maps, afim de alcançar melhores performances a nível de tempo de pesquisa e consumo de memória, assim como obter caminhos visualmente mais suaves

Supply chain information visibility and its impact on decision-making : an integrated model in the pharmaceutical industry : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

Supply chain information visibility (SCIV) has been largely recognized as a key issue in pharmaceutical supply chain management. In recent years, there has been growing concern regarding the exponential growth and ubiquity of supply chain information as the result of the application of advanced technologies. Thus, the topic of visibility of information flow across a supply chain has attracted interest in both practice and academia. Despite the existence of considerable literature on SCIV, the concept is still under-theorized. The lack of a clear understanding of the characteristics of SCIV has made it difficult to evaluate the effectiveness of SCIV and, consequently, hinders the improvement of SCIV (McIntire, 2014). Second, recent research identifies the potential of SCIV for operational performance through supporting managerial decision-making but also points out challenges and risks. In addition, there is a dearth of behavioral empirical research on supply chain management topics with which to achieve an increase in theory-building research in the field. This research addresses these gaps in the literature and investigates how SCIV across the pharmaceutical supply chain is perceived by pharmaceutical supply chain practitioners who are involved in supply chain decision-making, and how the decision-makers make use of SCIV in their supply chain decision-making process. This study adopted an exploratory, and qualitative approach to address two research questions: “How do supply chain professionals perceive SCIV in the pharmaceutical supply chain?” and “How do supply chain professionals make informed supply chain decisions?” The constructivist grounded theory methodology was used to guide the data gathering and analysis. The data were mainly drawn from semi-structured interviews with supply chain practitioners in New Zealand-based pharmaceutical firms, working at different levels of the supply chain, including manufacturers and distributors. Based on the findings a theoretical model was developed, the Pharmaceutical Supply Chain Information-based Decision-Making Model. The model explains the behavioral supply chain decision-making process in the pharmaceutical supply chain, based on the existence of a given level of SCIV. The empirical findings suggest that SCIV is achieved both within and outside of the pharmaceutical firms and that human relational factors tend to be more beneficial than technological factors in developing SCIV. The importance of this finding is that it addresses a frequently asked question in recent literature about what constitutes SCIV and how to successfully build information visibility in a supply chain. Moreover, this research contributes to the behavioural supply chain management research literature by introducing a theoretical model of pharmaceutical supply chain information-based decision-making, which is grounded in the field data. The model offers significant theoretical insight into information-based decision-making in the pharmaceutical supply chain context based on empirical data, which has been largely overlooked in the supply chain management discipline. The empirical findings suggest that supply chain practitioners make information-based decisions in which they conduct an informative engaging mechanism with technological tools, with relevant stakeholders, and with themselves. Thus, the decision-making process involves extensive data analysis along with the crucial support of experience-based intuition and relevant stakeholders’ engagement. Another key contribution of this study is the identification of the constructive aspect of political behaviour in the supply chain decision-making process in which relevant stakeholders when invited to engage in the process tend to positively contribute and buy into the decision. Finally, this thesis provides significant practical implications and suggest directions for future research. Supply chain practitioners may benefit from the study by utilizing the study’s results to develop supply chain information visibility in their firms. In addition, the theoretical model of the information-based decision-making process explicates a useful step-by-step approach for supply chain practitioners to follow in making effective supply chain operational decisions. Recommendations for further research are provided, especially the recommendations for further studies that are crucially needed to assist firms to counter the pharmaceutical supply chain disruption risks caused by the Covid-19 pandemic

Embedded Sensors in the Landscape: Measuring On-site Plant Stress Factors

This paper investigates the production of low-cost environmental sensors to collect data on environmental factors influencing plant stress in designed landscapes. Parameters measured include soil moisture, humidity, temperature and solar exposure. A prototype sensor is constructed from available components and installed on a trial site in Sydney, Australia. Data received from the prototype sensor is integrated with a Landscape Information Model to provide ongoing post-occupancy feedback. Results indicate that such sensors are straightforward to assemble, and are cost effective. It is suggested that developing familiarity with this and other sensor applications has potential to improve landscape education and practice. Lack of uptake in the landscape professions is, as indicated by the literature, primarily resulting from lack of training and knowledge barriers. An implementation guide is proposed to address this gap

Field D* pathfinding in weighted simplicial complexes

Includes abstract.Includes bibliographical references.The development of algorithms to efficiently determine an optimal path through a complex environment is a continuing area of research within Computer Science. When such environments can be represented as a graph, established graph search algorithms, such as Dijkstra’s shortest path and A*, can be used. However, many environments are constructed from a set of regions that do not conform to a discrete graph. The Weighted Region Problem was proposed to address the problem of finding the shortest path through a set of such regions, weighted with values representing the cost of traversing the region. Robust solutions to this problem are computationally expensive since finding shortest paths across a region requires expensive minimisation. Sampling approaches construct graphs by introducing extra points on region edges and connecting them with edges criss-crossing the region. Dijkstra or A* are then applied to compute shortest paths. The connectivity of these graphs is high and such techniques are thus not particularly well suited to environments where the weights and representation frequently change. The Field D* algorithm, by contrast, computes the shortest path across a grid of weighted square cells and has replanning capabilites that cater for environmental changes. However, representing an environment as a weighted grid (an image) is not space-efficient since high resolution is required to produce accurate paths through areas containing features sensitive to noise. In this work, we extend Field D* to weighted simplicial complexes – specifically – triangulations in 2D and tetrahedral meshes in 3D