ADAPT: Abstraction Hierarchies to Succinctly Model Teamwork

© 2010 by Elbit Systems | Elbit Systems Proprietary

ADAPT: Abstraction Hierarchies to Succinctly Model Teamwork

Meirav Hadad1, Avi Rosenfeld2

2Department of Industrial Engineering,Jerusalem College of Technology,

Jerusalem, Israel

1Research Division, Elbit Systems Ltd,

Rosh Ha'Ayin 48091, Israel

[email protected], [email protected]


Modeling the Simulator’s Teamwork

How to Model Teamwork?SharedPlansSteamTaemsTeamcoreBite

Limitations of Previous Approaches:Often unclear how to create formal rules

How can we encode the expert’s knowledge

In previous approaches all rules needed to be defined in advance

Very limiting, especially when dynamics must be considered


Issues Specific to Simulation

Simulator often has relatively limited resourcesWhat is the size of the model?

What is the memory amount needed per agent?How fast is the processing time?Real time required!

Our goal is to simulate hundreds of agents


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1 2

3

Teamwork Example

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Autonomous Dynamic Agent Planning for TeamworkCreates two Hierarchical Planners – a group and task plannerUses abstract search techniques to generally define hierarchiesOnly during run time are the exact constraints addressed (elaboration)

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Our Solution: ADAPT


1 2

4 3


ADAPT: What is Novel?

ADAPT is an A.I. Teamwork EngineUses 3 steps

Branching Step (very big number of states)Refinement Step (add the constraints)Pruning Step (very small model)

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Branching Step

Identifies possible methods for expanding partial planGet all the possible methods from library of task methods and group methodsExample of possible task methods for air attack

Option 1 Option 2


Refinement Step

Adding information based on constraints (DCOP)1. Receive the set of the all possible task methods2. Receive the set of the all possible group methods3. Match the group methods to the task methods:

1. make intersection between the constraints of task and group methods 2. match the sub-tasks’ constraints of to sub-groups, create sub-

constraint vector c1, c2, …, cn for each matching

4. Each group member checks its matching level for each sub-constraint vector and put its matching grade at each method


Refinement Step


Pruning Step Remove Unpromising Candidates

1. Assignment and Matching Tables are built for each method and each method is graded according to the Task Assignment algorithm

2. The method with the greatest assignment grade is selected to be elaborated

3. If successful, association is done


Pruning Step


A High Level Overview of the Simulator

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Simulation Engine

AI EngineADAPT

Group KB Editor

Task KB Editor

Real Time Control

Group DB

Task DB


World S

tate

AI EngineCooperation Level

Decision Maker

Association

TaskPlanner

GroupPlanner

Constraints

Task Plan

Constraints

Group plan

GroupKB

TaskKB

Real TimeControl

FailuresHandler

Perception

Action

Re-planning\

Reassignment

recommendation

General Description of an ADAPT Agent


Teamwork State Results

BITE ADAPT Max ADAPT Average

#of Agents Task Group Task Group Task Group

5 561 18 44 5 37.1 3.67

8 624 146 53 8 39.65 6.29

12 829 400 68 8 56.86 6.17


Actual Run Times

Low Change Middle Change High Change0

0.02

0.04

0.06

0.08

0.1

0.12

Total TimeTask PlannerGroup Planner

Tim

e (s

econ

ds)


ADAPT = teamwork into group and task plannersUses abstract search to build model incrementallyLarge savings in teamwork model size

Example how ADAPT used in a realistic simulation domainHow can ADAPT be used in other domains?Are the savings dependent on domain specifics?

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Conclusion and Future Work

Documents

ADAPT: Abstraction Hierarchies to Succinctly Model Teamwork