DrillSim: A Testbed for Crisis Response Activity Simulation

1. DrillSim July 2005

2. DrillSim: a Testbed • DrillSim simulates a crisis response activity (e.g, evacuation). • DrillSim allows testing IT solutions in the context of the simulated response activity. • Translates IT metrics to response metrics. • E.g., Bandwidth usage, time, accuracy to casualties, response time, risks taken.

3. DrillSim: a multi-agent system • One agent simulates one person. • Multiple roles: • Evacuee • Floor warden • Building coordinator • Zoon captain • Etc. • Individual profiles (physical and cognitive abilities) • Relationship between agents -> social networks. • Agents involved in information flow • Collect information via their own sensors (e.g., eyes), or the devices they carry (e.g., PDA) • Analyze information (i.e., assimilate information, make decisions) • Share and disseminate information via their own communication devices (e.g., speech) or the devices they carry (e.g., PDA)

4. DrillSim: a plug-and-play system • Plug models that drive simulation such as • human behavior, • spatio-temporal models (e.g., geography, class schedules) • crisis models, • response activity, • response plans. • Plug IT solutions. • Response activity projected as information flow • Information is • Collected • Analyzed • Shared • Disseminated • IT solutions can be plugged at any of these points

5. DrillSim: more than a simulator • DrillSim can simulate a drill • DrillSim can also be synchronized with an on-going drill: • Simulated drill augments an on-going drill adding • Simulated people • Simulated communications infrastructure • Simulated sensing infrastructure  Lowers cost of a real drill, adds flexibility On-going drill augments simulated drill Real people Real communications infrastructure Real sensing infrastructure  Adds realism, allows for calibration of the simulated entities

6. DrillSim: more than a simulator • DrillSim can simulate a drill • DrillSim can also be synchronized with an on-going drill: • Simulated drill augments an on-going drill adding • Simulated people • Simulated communications infrastructure • Simulated sensing infrastructure  Lowers cost of a real drill, adds flexibility • On-going drill augments simulated drill • Real people • Real communications infrastructure • Real sensing infrastructure  Adds realism, allows for calibration of the simulated entities

7. DrillSim Sensing Infrastructure Model DrillSim World Simulated World Agent Model Real world Environment Model Geography Model Observed World Crisis Model Initial Scenario Generator Calibrator Response Activity Module Resources Communication Media Model Scenario Model People

8. DrillSim Models • Models • Models to represent spatial temporal data • Geographical space, dynamic changes in the space, traffic, people, etc. • A response plan • Plan of evacuation. • A crisis model • Represent disasters and their effects. • Human Models • Models to represent agents (e.g., information assimilation, decision making, sensing capabilities).

9. Scenario Generator • Scenario is the location of people in the geographical space and their activities at the moment. • The initial scenario generates a scenario based on • Statistics like classes in universities • Pedestrian traffic • Information about meetings, games, etc • Location of response personnel • The scenario is generated by observing the physical world.

10. Response Activity Module • The main module that drives the simulator • Major components • Agents • Log of events • Plays out the activities based on the information in the observed world

11. The 4 worlds • Real world: an on-going drill • Simulated world: computer simulated drill • DrillSim world: merging of real world and simulated world. • Observed world: DrillSim world as observed by the agents DrillSim World Simulated World Real world Observed World

12. Calibrator • Calibrator is used to update the • Models used to represent agent behaviour • e.g: floor warden spent 5 min in each room • Decision making of the agent • Helps in Virtual reality, augmented reality integration • Agent behavior can be updated by its human counterpart’s behavior

13. DrillSim Sensing Infrastructure Model DrillSim World Simulated World Agent Model Real world Environment Model Geography Model Observed World Crisis Model Initial Scenario Generator Calibrator Response Activity Module Resources Communication Media Model Scenario Model People

14. Research Problems • Dynamic Data Management • Navigation • VR/AR Integration • Reasoning/Behavior Modeling • Languages • Data representation • Representing geography (at different scales) • Representing dynamic objects and phenomena • Architecture

15. Dynamic Data Management • There are continuous queries from different sources • The data is highly dynamic • Optimizing querying and updating in such scenario • Dynamic queries over mobile objects • Iosif • Querying imprecise data in moving object environments • Dmitry • Push Pull • Chen Li

16. Navigation • Navigation in the context of highly dynamic environments, and multiple agents • Should be optimal (compute/resource intensive) • Take into consideration real time changes • Representation for such planning algorithms • Modeling human navigation • Path planning for a group of agents • Optimizing location of agents, common goals, etc • flow control • Evacuation planning

17. VR/AR Integration Goal: Interaction between real and virtual worlds • Real to virtual • Immersing people into the simulator • Interface for real people to communicate with simulator • Tracking via sensors • Integrating real sensors • Integrating real communication devices and media • Calibration • Data from drills • Live actors • Virtual to real

18. Data representation • Representing geography (at different scales) • Representing dynamic objects and phenomena

19. Reasoning • Modeling human behavior • Decision making • Event based or taken every time unit? • Humans as information processors? • Social networks • Human communication • Representation of knowledge and awareness

20. Languages • Languages • Communication between agents and real people • Languages for specifying evacuation procedures and activities. • Integrating IT solutions.

21. Where we are: v0.2 • Response activity: evacuation • 4th floor Cal(IT)2 • Grid representation • A* algorithm + obstacle avoidance • Stochastic decision making based on ANN • Evacuee and floor-warden roles • 2D/3D interface • Human can control an agent • Human can communicate with agents • Simplified crisis model (spreading fire) • Response metric: number of people in building vs time

22. Demo

23. Where we are going • Campus-wide evacuation • Plug-and-play architecture • Scalable architecture • Integration with real world and real activities • Realistic models for crisis, agent behavior, and initial scenario generator.