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Demo for AAMAS-2012
GaTAC: A Scalable and Realistic Testbed
for Multiagent Decision Making
Ekhlas Sonu, Prashant Doshi
Dept. of Computer Science
University of Georgia
Athens, GA, 30602, USA
[sonu,pdoshi]@cs.uga.edu
Objective
To design and implement a realistic testbed to
evaluate the performance of decision making
algorithms in a problem domain that is:
Relevant in cooperative, competitive and mixed
settings
i.e. across different frameworks such as Dec-POMDP, IPOMDP, etc.
Scalable in problem size
No. of Physical States
Flexible in agent capabilities
Number and type of actions and observations
Extensible in number of agents and adaptable to
agent types
Motivation
Recently, there have been substantial
development in multi-agent decision making
algorithms that has driven researchers to go
beyond the traditional toy problem domains
such as the Tiger Problem, Machine
Maintenance Problem, Grid meeting, etc.
Some larger problem domains include
Cooperative Box-Pushing, Mars Rover, etc.:
Applied in cooperative settings
A Desirable Problem Domain
A desirable problem domain for multi-agent decision
making must be:
Scalable in physical states
Flexible in agent capabilities actions & observations
Extensible in number of agents
Relevant to cooperative, competitive and mixed settings
Able to produce solutions rich in structure
Realistic with a popular appeal
Proposed Scenario: Autonomous Unmanned
Aerial Vehicles
 Application:




Law enforcement [Murphy, Cycon; 1998]
Fighting forest fires [Casbeer, et.al.; 2005]
Border surveillance [Haddal, Gertler; 2010]
Wartime reconnaissance
 Uncertainty in AUAVs due to:
 Uncertainty about physical state
 Noisy actuators and sensors
 Added Complexity: Presence of other agents
 May be cooperative or competitive
 Related Research
 Focuses on formulating flight trajectories [R.
Bernard, et.al.,2002, 2003. S.M. Li, et.al 2002]
An example decision making scenario with AUAVs
We propose a problem
domain involving a
Autonomous Uninhabited
Aerial Vehicles
The operating theatre
may be divided into
various sectors (as is a
common practice) and
may be represented as a
grid of a predetermined
size
An example decision making scenario with AUAVs
An example UAV recon
problem may involve a UAV
(I) (or a team of UAVs) trying
to apprehend a target (T) (or
a team of moving targets)
while another team of UAVs
(J) tries to help the target(s)
escape to a safe house
Of course the exact
problem description is
flexible
S.H.
GaTAC: Overview
Georgia Testbed for Autonomous Control of vehicles
(GaTAC): computer simulation framework for evaluating solution to
a UAV reconnaissance problem. It provides:
 Hyperrealistic 3D rendering of AUAV acting in real world scenario
 Scalability in problem size and number of agents
 Flexibility in designing actions and observations of each agent
 Input:
 Agent control function (policies) for all agents generated by any
(multi-agent) decision making algorithm
 Output:
 Simulation of policies on a flight simulator
 Results of simulations may be compared for policies generated by
different algorithms using metrics such as number of captures, cumulative
reward, etc.
GaTAC: Workflow
We begin with a formal description for any UAV decision making problem
Formulate problem as
.dpomdp/.ipomdp file
Configure GaTAC for simulation
(i.e. setup environment)
GaTAC
GaTAC: Workflow
.dpomdp/.ipomdp
Solve using
algo. of choice
Obtain
policies
Policies for each agent are fed to
GaTAC to be simulated and evaluated
GaTAC
GaTAC: Workflow
.dpomdp/
.ipomdp
Solve
Simulate policies and evaluate results
using metrics such as number of
success, cumulative rewards, etc.
GaTAC
GaTAC Components
Each instance of GaTAC has three components:
Flight Simulator
Off-shelf open source flight simulator on which policies
are simulated
One instance of flight simulator for each agent
Autonomous Control Module
Control each aircraft and make it behave according to
the policy on the flight simulator
Communication Module
Send aircraft behavior from ACM to flight simulator
Communicate with other agents (if required)
GaTAC instances may run on different machines
Connected using communication module
Architecture
Communication
between agents
Flight Simulator
Communication Autonomous
Module
Control Module
Flight Simulator
FlightGear:
Open-source (written in C++)
Multi-platform
Hyperrealistic 3D graphics
3D virtual map
Flexible with choices of
Multiple models of aircrafts
Locations to act as operating environment
Weather condition, time of day, etc.
6 DOF flight dynamics model
Simulates effects of airflow on different parts of
aircraft
FlightGear in Operating Scenario
 FG utilizes realistic 3D
scenery available from
TerraGear
 Provides multiple view of
the flying aircraft
 Cockpit view, tail view, etc.
 Multiple instances of FG
may be linked together
through external serversideal for multi-agent
settings
Autonomous Control Module
Used to algorithmically control the aircraft and make it
behave according to policy: 3 levels of hierarchy
Agent Actions on Grid
High Level Actions
Takeoff, Fly-Straight, Turn, Change Altitude
Low Level Actions
Control Rudder, Throttle, Aileron, Roll, Pitch, etc.
Perform
Perform
simple
low level
tasks
actions
that
represent
to
control
simple
aircraft
by adjusting
behaviors
Actions
constructed
using
high
level
actions
toaircraft
represent
actions
parameters
the at
6DoF
of agents
in thealong
problem
hand
Communication Module
 Establish a communication channel between:
 Autonomous Control Module and FlightGear
 Between each agent (if required e.g. in team settings)
 Communication channels use UDP, httpd and XML
 Communicate low-level flight control data from an
instance of autonomous control module to
respective instance of FlightGear
 Communicate aircraft position to all other
instances of GaTAC in real time (used to formulate
observations)
Communication Module Functions
 Send control data from ACM to FG
 May adjust flight parameters such as controlling
thrust, rudder, aileron, altitude, etc.
 Receive the aircraft’s flight dynamics in real time
from FG and send to ACM for path correction
 Position , aircraft orientation on 6 DoF, flight speed,
altitude, etc.
 May be used to pass messages between GaTAC
instances (when communication between agents
is required)
GaTAC Control Algorithm
Get Observations/
Next Action
Read policy from file
Fly according to policy
Observation
=Successful?
Start FlightGear
No
Obtain
Agentaction
actiontosystematically
perform
Next
Repeat
action
untilmay
termination
be obtained from
broken
from
down
theinto
policy
high-level and
policy
condition
using
reached
the observation
then low-level actions to control
the aircraft algorithmically
Yes
Mission
Accomplished
Conclusions
GaTAC:
Can act as an open-source testbed for decision theoretic agents
May be used to compare different algorithms irrespective of decision
making framework (Dec-POMDP, I-POMDP, MTDP, etc.)
Is extensible: no upper bound on size of problem
No. of physical states, no. of agents, no. and types of actions & observations
Facilitates deployment of decision theoretic agents in hyper-realistic real
world settings (cooperative, competitive, or mixed)
Easily configurable for simulating any UAV problem
Provides for communication between agents
May be extended to include choice of locations and aircrafts
Demo
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