DrillSim

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 -gt 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
Communication Media Model
Resources
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
  counterparts 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
Communication Media Model
Resources
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.

24
(No Transcript)