Multiagent Simulations for Emergency Situations in Buildings

Proceedings of: 13th Ibero-American Conference on Artificial Intelligence (IBERAMIA 2012): Workshop on Intelligent systems for context-based information fusion (ISCIF). Cartagena de Indias, Colombia. 13-16 November 2012.This paper presents a multi-agent framework using NetLogo to simulate human and collective behaviors during emergency evacuations. Emergency situation appears when an unexpected event occurs. In indoor emergency situation, evacuation plans de ned by facility manager explain procedure and safety ways to follow in an emergency situation. Critical and public scenarios are buildings where there is an everyday transit of thousands of people. In this case the importance is related with incidents statistics regarding overcrowding and crushing in public buildings. Simulation has the objective of evaluating building layouts considering several possible con gurations. Agents could be based on reactive behavior like avoid danger or follow other agent, or in deliberative behavior based on BDI model. This tool provides decision support in a real emergency scenario like an public buildings, analyzing alternative solutions to the evacuation process.Publicad

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes

An agent-based decision support framework for a prospective analysis of transport and heat electrification in urban areas

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users’ behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents’ behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities

Modeling the Environmental Impact of Sustainability Policies in the Construction Industry Using Agent Based Simulation and Life Cycle Analysis

The construction industry, with its long supply chain and long lifetime of projects, is blamed to be one of the main contributors to environmental concerns including accelerated resource consumption and harmful emissions. Industry stakeholders, including developers, designers, contractors and suppliers, are, therefore, continuing to explore different options to reduce this impact. Various approaches have been adopted in different countries with building rating systems like the Leadership in Energy & Environmental Design (LEED) certification program being the most common way reflecting stakeholders’ efforts to go green. Governments and concerned authorities at national and state levels are expected to foster the trend of sustainable construction by motivating these stakeholders and pursuing policies that would help the green momentum. However, decision makers at such governmental and state levels face a challenge of prioritizing the policies and regulations that should be imposed. The objective of this paper is to present the development of a framework of an Agent Based Model (ABM) that simulates the effect of different possible policies in the construction market using Life Cycle Analysis (LCA), which is to be used by decision makers to assess and prioritize different policies or combination of policies. The framework was developed using Anylogic software and a sample construction market from the state of Qatar was used as an example for implementing the proposed framework. Results of running the model on this sample market illustrate the effectiveness of using this ABM as a support tool for decision makers in the area of sustainable construction

From systems to patterns and back - Exploring the spatial role of dynamic time and direction patterns in the area of regional planning

This master thesis presents a data-driven framework to explore the role of dynamic time and direction patterns in the area of Finnish Lapland in order to improve decision-making in urban planning and design tasks. The Arctic Ocean Railway project is chosen as a case study. In an era marked by dramatic environmental, political and societal changes, the Arctic region becomes more global and complex. An increasing number of actors are involved in its spatial transformations. Due to melting ice, the Northern Sea Route gains attention from the shipping and trade industries that are manifested in new port and infrastructure projects. Eco-tourism is booming in the Arctic due to its imaginary remoteness, while local Indigenous People try to preserve traditional livelihoods. In order to cope with the increasing complexity of such dynamic urban and regional challenges, Systems Thinking, dynamic patterns, modelling and use of simulation are researched to open up novel ways for complex regional planning methods. This is achieved by designing an agent-based model and using different representation and abstraction features for different dynamic data packages. The project is integrated within the GAMA simulation platform (a modelling and simulation development environment for building spatially explicit agent-based simulations) and embedded in the MIT CityScope framework - a medium for both, analyzing agent’s behavioural patterns and displaying them to the relevant stakeholders. The project attempts to address the necessity to handle the increasing complexity by presenting a dynamic, evidence-based planning and decision support tool called CityScope Lapland. The main goal of CityScope Lapland is to use digital technologies to incorporate variables like time and direction in urban spatial analysis and methodology; secondly, to improve the accessibility of the decision-making process for non-experts through a tangible user interface, and third, to help users evaluate their decisions by creating a feedback through real-time visualization of urban simulation results when facing less and less predictable futures. The project provides an alternative design approach, introducing new forms of urban imagination and different ways of perceiving and measuring complex spatial transformations

Hybrid intelligent decision support system for distributed detection based on ad hoc integrated WSN & RFID

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe real time monitoring of environment context aware activities, based on distributed detection, is becoming a standard in public safety and service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt immediate reaction to potential hazards identified in real time, at an early stage to engage appropriate control actions. Effective emergency response can be supported only by available and acquired expertise or elaborate collaborative knowledge in the domain of distributed detection that include indoor sensing, tracking and localizing. This research proposes a hybrid conceptual multi-agent framework for the acquisition of collaborative knowledge in dynamic complex context aware environments for distributed detection. This framework has been applied for the design and development of a hybrid intelligent multi-agent decision system (HIDSS) that supports a decentralized active sensing, tracking and localizing strategy, and the deployment and configuration of smart detection devices associated to active sensor nodes wirelessly connected in a network topology to configure, deploy and control ad hoc wireless sensor networks (WSNs). This system, which is based on the interactive use of data, models and knowledge base, has been implemented to support fire detection and control access fusion functions aimed at elaborating: An integrated data model, grouping the building information data and WSN-RFID database, composed of the network configuration and captured data, A virtual layout configuration of the controlled premises, based on using a building information model, A knowledge-based support for the design of generic detection devices, A multi-criteria decision making model for generic detection devices distribution, ad hoc WSNs configuration, clustering and deployment, and Predictive data models for evacuation planning, and fire and evacuation simulation. An evaluation of the system prototype has been carried out to enrich information and knowledge fusion requirements and show the scope of the concepts used in data and process modelling. It has shown the practicability of hybrid solutions grouping generic homogeneous smart detection devices enhanced by heterogeneous support devices in their deployment, forming ad hoc networks that integrate WSNs and radio frequency identification (RFID) technology. The novelty in this work is the web-based support system architecture proposed in this framework that is based on the use of intelligent agent modelling and multi-agent systems, and the decoupling of the processes supporting the multi-sensor data fusion from those supporting different context applications. Although this decoupling is essential to appropriately distribute the different fusion functions, the integration of several dimensions of policy settings for the modelling of knowledge processes, and intelligent and pro-active decision making activities, requires the organisation of interactive fusion functions deployed upstream to a safety and emergency response.Saudi government, represented by the Ministry of Interior and General Directorate of Civil Defenc

A normative approach to multi-agent systems for intelligent buildings

Building Management Systems (BMS) are widely adopted in modern buildings around the world in order to provide high-quality building services, and reduce the running cost of the building. However, most BMS are functionality-oriented and do not consider user personalization. The aim of this research is to capture and represent building management rules using organizational semiotics methods. We implement Semantic Analysis, which determines semantic units in building management and their relationship patterns of behaviour, and Norm Analysis, which extracts and specifies the norms that establish how and when these management actions occur. Finally, we propose a multi-agent framework for norm based building management. This framework contributes to the design domain of intelligent building management system by defining a set of behaviour patterns, and the norms that govern the real-time behaviour in a building

Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making