Comparing Agent Architectures in Social Simulation: BDI Agents versus Finite-state Machines

Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Agent-based simulation is a powerful tool for decision-makers to explore different strategies for managing such crisis, testing them on a simulated population; but valid results require realistic underlying models. It is therefore essential to be able to compare models using different architectures to represent the human behaviour, on objective and subjective criteria. In this paper we describe two simulations of the Australian population\u27s behaviour in bushfires: one with a finite-state machine architecture; one with a BDI architecture. We then compare these two models with respect to a number of criteria

Agent-based Analysis of the Spread of Awareness in the Population in the Prodromal Phase of Bushfires

Efficient communication is essential in disasters in order to coordinate a response and assure effective evacuation. This paper focuses on the case study of the Melbourne bushfires in 2009. We first analysed some interviews of the population to know who the population communicates with (neighbours, family, authorities, etc), and using what channel (radio, phone, internet, etc). We then developed and implemented communicative actions in a Belief-Desire-Intention model of the population\u27s behaviour. Finally, we ran experiments in order to compare the speed at which the population becomes aware of the fires in different scenarios with different types of communication (more or less organised). Our first results show that more organised modes of communication would provide significant benefits in terms of propagation of awareness in the population

The role of cognitive biases in reactions to bushfires

International audienceHuman behaviour is influenced by many psychological factors such as emotions, whose role is already widely recog-nised. Another important factor, and all the more so during disasters where time pressure and stress constrain reasoning, are cognitive biases. In this paper, we present a short overview of the literature on cognitive biases and show how some of these biases are relevant in a particular disaster, the 2009 bushfires in the SouthEast of Australia. We provide a preliminary formalisation of these cognitive biases in BDI (beliefs, desires, intentions) agents, with the goal of integrating such agents into agent-based models to get more realistic behaviour. We argue that taking such "irrational" behaviours into account in simulation is crucial in order to produce valid results that can be used by emergency managers to better understand the behaviour of the population in future bushfires

Integrating BDI agents with Agent-based simulation platforms

Agent-Based Models (ABMs) is increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is exible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community

Integrating BDI agents with agent-based simulation platforms

This paper describes an integration framework that allows development of simulations where the cognitive reasoning and decision making is programmed and executed within an existing BDI (Belief, Desire, Intention) system, and the simulation is played out in an existing ABM (Agent Based Modelling) system. The framework has a generic layer which manages communication and synchronisation, a system layer which integrates specific BDI and ABM systems, and the application layer which contains the program code for a particular application. The code is available on GitHub: https://github.com/agentsoz/bdi-abm-integratio

Agent-based simulation of pedestrians' earthquake evacuation; application to Beirut, Lebanon

Most seismic risk assessment methods focus on estimating the damages to the built environment and the consequent socioeconomic losses without fully taking into account the social aspect of risk. Yet, human behaviour is a key element in predicting the human impact of an earthquake, therefore, it is important to include it in quantitative risk assessment studies. In this study, an interdisciplinary approach simulating pedestrians' evacuation during earthquakes at the city scale is developed using an agent-based model. The model integrates the seismic hazard, the physical vulnerability as well as individuals' behaviours and mobility. The simulator is applied to the case of Beirut, Lebanon. Lebanon is at the heart of the Levant fault system that has generated several Mw>7 earthquakes, the latest being in 1759. It is one of the countries with the highest seismic risk in the Mediterranean region. This is due to the high seismic vulnerability of the buildings due to the absence of mandatory seismic regulation until 2012, the high level of urbanization, and the lack of adequate spatial planning and risk prevention policies. Beirut as the main residential, economic and institutional hub of Lebanon is densely populated. To accommodate the growing need for urban development, constructions have almost taken over all of the green areas of the city; squares and gardens are disappearing to give place to skyscrapers. However, open spaces are safe places to shelter, away from debris, and therefore play an essential role in earthquake evacuation. Despite the massive urbanization, there are a few open spaces but locked gates and other types of anthropogenic barriers often limit their access. To simulate this complex context, pedestrians' evacuation simulations are run in a highly realistic spatial environment implemented in GAMA [1]. Previous data concerning soil and buildings in Beirut [2, 3] are complemented by new geographic data extracted from high-resolution Pleiades satellite images. The seismic loading is defined as a peak ground acceleration of 0.3g, as stated in Lebanese seismic regulations. Building damages are estimated using an artificial neural network trained to predict the mean damage [4] based on the seismic loading as well as the soil and building vibrational properties [5]. Moreover, the quantity and the footprint of the generated debris around each building are also estimated and included in the model. We simulate how topography, buildings, debris, and access to open spaces, affect individuals' mobility. Two city configurations are implemented: 1. Open spaces are accessible without any barriers; 2. Access to some open spaces is blocked. The first simulation results show that while 52% of the population is able to arrive to an open space within 5 minutes after an earthquake, this number is reduced to 39% when one of the open spaces is locked. These results show that the presence of accessible open spaces in a city and their proximity to the residential buildings is a crucial factor for ensuring people's safety when an earthquake occurs

Exploring the adaptive capacity of emergency management using agent based modelling

This project aimed to explore the suitability of Agent Based Modelling and Simulation (ABMS) technology in assisting planners and policy makers to better understand complex situations with multiple interacting aspects. The technology supports exploration of the impact of different factors on potential outcomes of a scenario, thus building understanding to inform decision making. To concretise this exploration a specific simulation tool was developed to explore response capacity around flash flooding in an inner Melbourne suburb, with a focus on sandbag depots as an option to be considered.The three types of activities delivered by this project to achieve its objectives were the development of an agent-based simulation, data collection to inform the development of the simulation and communication and engagement activities to progress the work. Climate change is an area full of uncertainties, and yet sectors such as Emergency Management and many others need to develop plans and policy responses regarding adaptation to these uncertain futures. Agent Based Modelling and Simulation is a technology which supports modelling of a complex situation from the bottom up, by modelling the behaviours of individual agents (often representing humans) in various scenarios. By running simulations with different configurations it is possible to explore and analyse a very broad range of potential options, providing a detailed understanding of potential risks and outcomes, given particular alternatives. This project explored the suitability of this technology for use in assessing and developing the capacity of the emergency response sector, as it adapts to climate change. A simulation system was developed to explore a particular issue regarding protection of property in a suburb prone to flash flooding. In particular the option of providing sandbag depots was explored. Simulations indicated that sandbag depots provided by CoPP or VicSES were at this time not a viable option. The simulation tool was deemed to be very useful for demonstrating this to community members as well as to decision makers. An interactive game was also developed to assist in raising awareness of community members about how to sandbag their property using on-site sandbags. The technology was deemed to be of great potential benefit to the sector and areas for further work inorder to realise this benefit were identified. In addition to developing awareness of useful technology, this project also demonstrated the critical importance of interdisciplinary team work, and close engagement with stakeholders and end users, if valuable technology uptake is to be realised. &nbsp

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

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