Distributed Agent-Based Building
Evacuation Simulator
A. Filippoupolitis, E. Gelenbe, D. Gianni,
L. Hey, G. Loukas, S. Tim...
Presentation Overview
 Emergency management
 Motivation
 Simulated Model
 Simulation Framework – Building Evacuation
S...
Building Evacuation Scenario
Scenario characteristics:
• Multi-storey building
• Emergency situation
• Civilians try to ev...
Motivation
 Decentralised optimisation techniques that will support
actors during dynamic and rapidly changing situations...
Simulated Model
 The model includes:
• A world Model, which represents the physical space
inside the building and its sta...
World Model
 A graph models the physical space:
• The nodes of the graph represent “Points of
Interest”
• Each edge repre...
Example of World Model
 Each node has a queue of human agents, attributes for fire, x and y
coordinates in the space, typ...
Types of Agents
 Three type of agents
• Resource Manager, which manages the
access to the world nodes
• Human agents, whi...
Resource Manager (RM)
 RM is in charge of:
• Coordinating the access to the nodes
• Providing world updates for each agen...
Human Agents (HAs)
HAs are characterised by:
 A personal view of the world
 One or more goals (including the decision
mo...
Going Distributed
 Why? The amount of computational resources
grows at least as polynomial function of the
number of simu...
Model Partitioning
 We follow three guidelines:
• Exploiting the intrinsic parallelism of independent
physical subsystem
...
Model Adaptation
 The performance of the simulator are affected
by the amount of data exchanged
 Reduce such data by:
• ...
SimJADE
 SimJADE is:
• A Java framework for Agent-based M&S
• JADE-based, thus FIPA compliant
 It offers a formulation o...
SimJADE components
It is defined through:
 A simulation ontology
• Simulation time, simulation services
 A simulation ag...
• A virtual hazard (fire, gas, etc.) spreads
inside the physical world
• A real Wireless Sensor Network test-bed
monitors ...
• Two different representations of the virtual hazard spreading (sensed , actual):
- The Building Evacuation Simulator (BE...
Current Building Evacuation Simulator
Floor1
Floor2
Floor3
Floor4
Stairs
A
Stairs
C
Stairs B
Point of Collection
…
Current applications
• Adaptive on-line decision
support for building evacuation
• Optimal allocation of rescuers
to injur...
Conclusions
 Develop decentralised optimisation techniques that can provide
decision support during an emergency situatio...
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Distributed Building Evacuation Simulator

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Conference Presentation at the 2008 Summer Computer Simulation Conference

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Distributed Building Evacuation Simulator

  1. 1. Distributed Agent-Based Building Evacuation Simulator A. Filippoupolitis, E. Gelenbe, D. Gianni, L. Hey, G. Loukas, S. Timotheou {gianni, e.gelenbe}@imperial.ac.uk Intelligent Systems and Networks Group Imperial College London
  2. 2. Presentation Overview  Emergency management  Motivation  Simulated Model  Simulation Framework – Building Evacuation Simulator (BES)  Integration with a wireless sensor network  Conclusions
  3. 3. Building Evacuation Scenario Scenario characteristics: • Multi-storey building • Emergency situation • Civilians try to evacuate following the quickest and safest path to the exit, while adapting to the events • Emergency personnel enter the building trying to rescue civilians and extinguish fire
  4. 4. Motivation  Decentralised optimisation techniques that will support actors during dynamic and rapidly changing situations  We want to carry out systematic investigations of such techniques in largely populated scenarios  A framework is needed, that allows: 1. Reproducibility of experiments 2. Extendibility to diverse scenarios 3. Distributed operation (for largely populated scenarios)
  5. 5. Simulated Model  The model includes: • A world Model, which represents the physical space inside the building and its status • One or more hazard agents, which affect the status of the world • A population of human agents, which move and cooperate inside the physical world according to personal characteristics
  6. 6. World Model  A graph models the physical space: • The nodes of the graph represent “Points of Interest” • Each edge represents a physical path between two nodes  Graph elements are enriched with a set of attributes that represent the status of the world
  7. 7. Example of World Model  Each node has a queue of human agents, attributes for fire, x and y coordinates in the space, type of node (e.g. door, stairs), etc.  Each edge has: list of human agents traversing it, attributes for fire, etc.
  8. 8. Types of Agents  Three type of agents • Resource Manager, which manages the access to the world nodes • Human agents, which move inside the physical world • Hazard agents, which affect the status of the world
  9. 9. Resource Manager (RM)  RM is in charge of: • Coordinating the access to the nodes • Providing world updates for each agent  RM is defined by a simple wait event/process event logic that proceeds until the simulation ends
  10. 10. Human Agents (HAs) HAs are characterised by:  A personal view of the world  One or more goals (including the decision models on how to achieve them)  Motion model  Health model
  11. 11. Going Distributed  Why? The amount of computational resources grows at least as polynomial function of the number of simulated agents  Two major modelling issues to face: • Model partitioning • Model adaptation to the distributed environment
  12. 12. Model Partitioning  We follow three guidelines: • Exploiting the intrinsic parallelism of independent physical subsystem • Meeting local memory constraints • Minimising the network workload  The simulated world is allocated on independent single area simulators (floor or stairs) running on a separate host
  13. 13. Model Adaptation  The performance of the simulator are affected by the amount of data exchanged  Reduce such data by: • Locally store “constant” data • Move only individual knowledge  Agents also interact with local world only  Locally, condensed representation of the remote world (GPoI)
  14. 14. SimJADE  SimJADE is: • A Java framework for Agent-based M&S • JADE-based, thus FIPA compliant  It offers a formulation of discrete event simulation systems in terms of MAS through its components  It also provides a uniform interface for MAS and Agent-based M&S, easing therefore the development of such simulators
  15. 15. SimJADE components It is defined through:  A simulation ontology • Simulation time, simulation services  A simulation agent society • Simulation engine (local/distributed), which orchestrates the simulation • Simulation entities, which incorporate the logic  An interaction protocol between the agents, implemented by a set of behaviours and simulation event handlers
  16. 16. • A virtual hazard (fire, gas, etc.) spreads inside the physical world • A real Wireless Sensor Network test-bed monitors the spreading of the hazard • Each sensor is assigned to a vertex on the graph of the emergency response simulator (e.g. like a room's smoke detector) • We use light from LEDs to represent the hazard within the virtual building • The hazard agent controls the hazard spreading and the intensity of the respective LEDs, providing input to the sensors regarding the intensity of the hazard Wireless Sensors Test Bed Integration (1)
  17. 17. • Two different representations of the virtual hazard spreading (sensed , actual): - The Building Evacuation Simulator (BES) connects to the wireless sensor network, processes the sensor readings and updates the sensed representation of hazard spreading - The actual data from the fire simulator are also processed by the BES in order to update the actual representation of hazard spreading • Effects of hazard spreading: - Over simulated time the paths become more hazardous and “slower” to traverse - When actors move along an edge with increased degree of danger, their health level decreases - Excessive exposure to danger results in a fatality Wireless Sensors Test Bed Integration (2)
  18. 18. Current Building Evacuation Simulator Floor1 Floor2 Floor3 Floor4 Stairs A Stairs C Stairs B Point of Collection …
  19. 19. Current applications • Adaptive on-line decision support for building evacuation • Optimal allocation of rescuers to injury locations
  20. 20. Conclusions  Develop decentralised optimisation techniques that can provide decision support during an emergency situation  Such techniques require a systematic investigation before being deployed in real scenarios  Cost and time effective investigations require a software framework that combines: • experiment reproducibility • high level of extendibility • distributed operation  We presented the Building Evacuation Simulator, a simulation framework that meets such requirements, and some basic examples of use

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