Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

Learning to Identify Bugs in Video Games

The use of intelligent software agents promises to revolutionise video game testing. While agents automate the time-consuming task of repeatedly playing a game in search of issues, humans can spend their time on the more creative aspects of game development. Despite the substantial advancements in game-playing that have made this possible, agents are reliant on humans, or hand-crafted guards, to determine whether there are issues with the game's design or functioning. This thesis aimed to develop testing agents that can identify issues with a game's function or bugs with minimal human involvement by learning from their prior experiences. The problem is framed as one of anomaly detection, where bugs correspond to abnormality or novelty in an agent's experience. A series of approaches based on Self-Supervised Learning and Causal Inference have been developed to enable an agent to measure abnormality or otherwise model the game to subsequently identify bugs. The focus was on laying the foundations for testing agents that operate over the same input/output modalities as human testers. The approaches were evaluated by testing a diverse collection of purpose-built video games, where they successfully identified bugs from a broad class. This thesis is among the first work to investigate the use of machine learning in the context of video game bug identification. It presents an exposition of the problem of learning intended behaviour, and then endeavours to develop solutions that demonstrate the benefits of using agents with learning capabilities for testing. Namely, ease of reuse across projects (reusability) and in identifying bugs that would otherwise require human involvement to be found (capability). The use of agents equipped with sophisticated game-playing algorithms and the identification tools outlined in this thesis offers a new framework for video game testing

Exploring visual representation of sound in computer music software through programming and composition

Presented through contextualisation of the portfolio works are developments of a practice in which the acts of programming and composition are intrinsically connected. This practice-based research (conducted 2009–2013) explores visual representation of sound in computer music software. Towards greater understanding of composing with the software medium, initial questions are taken as stimulus to explore the subject through artistic practice and critical thinking. The project begins by asking: How might the ways in which sound is visually represented influence the choices that are made while those representations are being manipulated and organised as music? Which aspects of sound are represented visually, and how are those aspects shown? Recognising sound as a psychophysical phenomenon, the physical and psychological aspects of aesthetic interest to my work are identified. Technological factors of mediating these aspects for the interactive visual-domain of software are considered, and a techno-aesthetic understanding developed. Through compositional studies of different approaches to the problem of looking at sound in software, on screen, a number of conceptual themes emerge in this work: the idea of software as substance, both as a malleable material (such as in live coding), and in terms of outcome artefacts; the direct mapping between audio data and screen pixels; the use of colour that maintains awareness of its discrete (as opposed to continuous) basis; the need for integrated display of parameter controls with their target data; and the tildegraph concept that began as a conceptual model of a gramophone and which is a spatio-visual sound synthesis technique related to wave terrain synthesis. The spiroid-frequency-space representation is introduced, contextualised, and combined both with those themes and a bespoke geometrical drawing system (named thisis), to create a new modular computer music software environment named sdfsys

Metaontology

The Ontological Question 'What exists?' dates back over two thousand five hundred years to the dawn of Western philosophy, and attempts to answer it define the province of ontology. The history of the Western philosophical tradition itself has been one of the differentiation and separation of the various sciences from the primordial stuff of ancient philosophy. Physics was first to break away from the tutelage of philosophy and established its independence in the seventeenth century. The other sciences followed suit fairly rapidly, with perhaps psychology being the last to separate. The results for modern philosophy - of this breakup of what was once a great empire over human reason - have been mixed. An inevitable result has been that questions considered in ancient times to belong to philosophy have fallen within the ambit of other disciplines. So speculations about the material composition and genesis of the universe that interested Thales, Heraclitus and Leucippus, are continued by contemporary cosmologists in well equipped research laboratories, and not by philosophers. However ontology, unlike cosmology, has not broken away from its parent discipline and the Ontological Question as to what exists is still argued by philosophers today. That ontology has failed to make the separation that cosmology has, is a reflection on the weakness of the methodology for settling ontological arguments. Unlike their great Rationalist predecessors, most modern philosophers do not believe that logic alone is sufficient to provide an answer as to what is. But neither do observation or experiment, in any direct way, seem to help us in deciding, for example, whether sets or intentions should be admitted to exist or not. In consequence, the status of ontology as an area of serious study has to depend on the devising of a methodology within which the Ontological Question can be tackled. The pursuit of such a methodology is the concern of metaontology and is also the concern of this thesis

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research