23 research outputs found

    A Behavioural Decision-Making Framework For Agent-Based Models

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    In the last decades, computer simulation has become one of the mainstream modelling techniques in many scientific fields. Social simulation with Agent-based Modelling (ABM) allows users to capture higher-level system properties that emerge from the interactions of lower-level subsystems. ABM is itself an area of application of Distributed Artificial Intelligence and Multiagent Systems (MAS). Despite that, researchers using ABM for social science studies do not fully benefit from the development in the field of MAS. It is mainly because the MAS architectures and frameworks are built upon cognitive and computer science foundations and principles, creating a gap in concepts and methodology between the two fields. Building agent frameworks based on behaviour theory is a promising direction to minimise this gap. It can provide a standard practice in interdisciplinary teams and facilitate better usage of MAS technological advancement in social research. From our survey, Triandis' Theory of Interpersonal Behaviour (TIB) was chosen due to its broad set of determinants and inclusion of an additive value function to calculate utility values of different outcomes. As TIB's determinants can be organised in a tree-like structure, we utilise layered architectures to formalise the agent's components. The additive function of TIB is then used to combine the utilities of different level determinants. The framework is then applied to create models for different case studies from various domains to test its ability to explain the importance of multiple behavioural aspects and environmental properties. The first case study simulates the mobility demand for Swiss households. We propose an experimental method to test and investigate the impact of core determinants in the TIB on the usage of different transportation modes. The second case study presents a novel solution to simulate trust and reputation by applying subjective logic as a metric to measure an agent's belief about the consequence(s) of action, which can be updated through feedback. The third case study investigates the possibility of simulating bounded rationality effects in an agent's decision-making scheme by limiting its capability of perceiving information. In the final study, a model is created to simulate migrants' choice of activities in centres by applying our framework in conjunction with Maslow's hierarchy of needs. The experiment can then be used to test the impact of different combinations of core determinants on the migrants' activities. Overall, the design of different components in our framework enables adaptations for various contexts, including transportation modal choice, buying a vehicle or daily activities. Most of the work can be done by changing the first-level determinants in the TIB's model based on the phenomena simulated and the available data. Several environmental properties can also be considered by extending the core components or employing other theoretical assumptions and concepts from the social study. The framework can then serve the purpose of theoretical exposition and allow the users to assess the causal link between the TIB's determinants and behaviour output. This thesis also highlights the importance of data collection and experimental design to capture better and understand different aspects of human decision-making

    Travel Decision Making Under Uncertainty and Road Traffic Behavior: The Multifold Role of Ambiguity Attitude

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    To aggregate commuters’ mode choices to traffic behavior in the presence of travel time uncertainty, we develop a dynamic traffic simulation in terms of an agent-based model, which consists of two sub-models, the mode choice model and the traffic flow simulation model. The modeling framework accommodates the interplay between the two models and their co-evolution over time. We embed an extended list of empirical parameters including ambiguity/risk attitudes and heterogeneity , and time-money trade-offs within a rank-dependent and source-dependent utility framework to imitate commuters’ daily mode choice behaviors. The improved behavioral realism at the micro-level results in an improved understanding of traffic flow in terms of modal split and average speed at equilibrium, compared to a conventional model which assumes risk neutrality and ambiguity neutrality. A novel finding is that ambiguity seeking, a typical behavior in the loss domain but largely ignored in the transport literature, acts as an important driver that shifts commuters from cars to public transport

    Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

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    The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

    Pedestrian Evacuation: Vulnerable Group Member Influence on the Group Leaders’ Decision-Making and the Impact on Evacuation Time

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    As pedestrian evacuations of buildings, outdoor venues, and special events occur, dynamic interactions between pedestrians and vehicles during egress are possible. To model pedestrian and vehicle evacuations, simulation models have evolved to incorporate more realistic crowd characteristics and behaviors to provide improved results. Past studies using modeling and simulation, specifically agent-based modeling, have explored pedestrian behaviors such as decision-making, navigation within a virtual environment, group formations, intra-group interactions, inter-group dynamics, crowd behaviors such as queuing and herding, and pedestrianvehicle interactions. These studies have led to relevant insights helpful to improving the accuracy of evacuation times for normal and emergency egress for preparedness and management purposes. As evacuating crowds are composed of individual pedestrians and social or familial groups, this project contributes to the study of pedestrian evacuation by exploring the incorporation of a subgroup not often considered in this area. Vulnerable individuals, such as the physically disabled, elderly, and children, can change the decision-making dynamic of a group leader while evacuating to safety. Current agent-based simulation models explore the intra- and inter- action and the effects on evacuation times; however, the vulnerable group members\u27 influence is neglected. This project presents enhancements to pedestrian evacuations with vehicle interaction using an agent-based simulation model that includes the presence of vulnerable group members and their impact on decision-making and evacuation times. This project explores how changing behaviors due to the presence of vulnerable group members can collectively cause delays and increase evacuation times. Utilizing verification and validation methods, the credibility and reliability of the simulation model and its results are increased. The results show that the group leaders\u27 decision-making differs when leading a vulnerable group versus a non-vulnerable group. Also, evacuation times increase with increased percentages of vulnerable groups within an evacuating crowd. A simulation tool can be utilized by end-users to explore specific evacuation scenarios in preparation for upcoming events and glean insight into how evacuation times may vary with differing crowd population sizes and compositions. Including vulnerable pedestrians in simulation models for evacuations would improve output accuracy and ultimately improve event training and preparation for future evacuations

    Urban Informatics

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    This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

    Urban Informatics

    Get PDF
    This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

    The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform

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    Wildfires are a significant safety risk to populations adjacent to wildland areas, known as the wildland-urban interface (WUI). This paper introduces a modelling platform called WUI-NITY. The platform is built on the Unity3D game engine and simulates and visualises human behaviour and wildfire spread during an evacuation of WUI communities. The purpose of this platform is to enhance the situational awareness of responders and residents during evacuation scenarios by providing information on the dynamic evolution of the emergency. WUI-NITY represents current and predicted conditions by coupling the three key modelling layers of wildfire evacuation, namely the fire, pedestrian, and traffic movement. This allows predictions of evacuation behaviour over time. The current version of WUI-NITY demonstrates the feasibility and advantages of coupling the modelling layers. Its wildfire modelling layer is based on FARSITE, the pedestrian layer implements a dedicated pedestrian response and movement model, and the traffic layer includes a traffic evacuation model based on the Lighthill-Whitham-Richards model. The platform also includes a sub-model called PERIL that designs the spatial location of trigger buffers. The main contribution of this work is in the development of a modular and model-agnostic (i.e., not linked to a specific model) platform with consistent levels of granularity (allowing a comparable modelling resolution in the representation of each layer) in all three modelling layers. WUI-NITY is a powerful tool to protect against wildfires; it can enable education and training of communities, forensic studies of past evacuations and dynamic vulnerability assessment of ongoing emergencies

    Adding BDI Agents to MATSim Traffic Simulator (Demonstration)

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    ABSTRACT MATSim is a mature and powerful traffic simulator, used for large scale traffic simulations, primarily to assess likely results of various infrastructure or road network changes. We have coupled MATSim with the BDI system GORITE to provide additional valuable functionality within MATSim, and have demonstrated this with an application combining BDI taxis with standard MATSim traffic agents. Although standard MATSim agents all have individual plans regarding their behaviour, this is fixed in advance, and cannot be modified during the simulation of a single day, depending on emergent situational phenomena. This limits its usability for a range of applications, such as evacuation planning. In addition, the plans of MATSim agents are very simple, being limited to sequences of activities at different locations. In this work we extend the capabilities of MATSim to allow agents to respond intelligently to emergent environmental phenomena
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