A Framework for Multi-agent A Framework for Multi-agent Discrete Event Simulation: V-Lab®Discrete Event Simulation: V-Lab®

Prasanna SridharShahab Sheikh-Bahaei

12/05/2003Autonomous Control Engineering

University of New Mexico

This work was supported by NASA AMES under grant NAG 2-1547

V-Lab®

• V-Lab® Environment Layers– Hardware Networking

• Networking foundation for inter-machine communication

– Middleware• Software environment for inter-

process communication

– IDEVS• Library of tools for soft

computing in DEVS formalism

– V-Lab®• Organizational structure for

DEVS objects. • Management objects to control

time and message flow in multi-agent systems

PHYSICAL NETWORK

MIDDLEWARE

I-DEVS

V-Lab

Inside Layered Architecture

middleware

AtomicModel

CoupledModel

simman

V-lab®

• Uses the IDEVS structure for creating objects

• Provides inbuilt agents and physics to model multi-agent multi-physics system

• SimMan is the message router to perform indirect message passing.

• Hierarchical Tree

SimEnv

Agents

Agents

Physics

Agents

SimMan Physics

SimMan and SimEnv

• SimEnv– Highest leveled coupled

model– Instatiates all other models– Houses all other models– Couples all other models

together

• SimMan– Message liaison providing

indirection between all other models

– Controls flow of time and messages between models for the simulation

SimEnvSimEnv

SimMan

Agent

Agent

Agent

Agent

Parsing

• A grammar defines rules for building syntactically correct programs.

• One way of defining grammar is through BNF (Backus-Naur Form)

• BNF of our simulation configurations is translated JavaCC format and saved as file parseConfig.jj

• When parseConfig.jj is compiled by JavaCC, it generates a Java file parseConfig.java. Parsing can be done by instatiating an object of parseConfig.java

Sample Config file

robo2 = Robot.Robot; //agent

robo3 = Robot.Robot; //agent

robo4 = Robot.Robot; //agent

robo5 = Robot.Robot; //agent

robo6 = Robot.Robot; //agent

robo7 = Robot.Robot; //agent

terr = Robot.Terrain;

Need for Agent Manager

4 agents connected together 18 agents connected

18 agents connected with manager

manager

Message routing

A1

SimMan

R1

A2

A3

A4

A5

R1:A2, A4, A5R2:A1,A3

R1

R1

R1

Legend:A1,A2,A3,A4,A5: AgentsR1: Request MessageR1: A2 , A4,… : Database for request/response

A1

SimMan

Re

A2

A3

A4

A5

R1:A2, A4, A5R2:A1,A3

Re

Re

Re

Legend:A1,A2,A3,A4,A5: AgentsRe: Response MessageR1: A2 , A4,… : Database for request/response

Re

Re

Potential repliers can be known by special messages

I-DEVS

DEVSJAVALibrary

Soft Computing

I-DEVSLibrary Extension

of DEVSJAVAclasses

Efficient wayof handlingTime parameters

Simulation Results

Fuzzy Logic ControllerFuzzy Logic Controller

DefuzzilierFuzzifierInference

Engine

Membership Functions

Fuzzy (IF THEN) RulesFuzzy VariablesLinguistic Variables

CrispValues

CrispValues

Devs-Fuzzy LogicDevs-Fuzzy Logic

DEVS primitives to compose a Mamdani rule:

Membership Functions

A typical Mamdani rule can be composed as follows i

1 1 2 2IF is AND is THEN is , for 1,2,...,i i ix A x A y B i l

X (real number)

Fuzzy Value

FuzzifierFuzzifier

Output Block

Output Block

Fuzzy Value

Y (Fuzzy set)

…

AND/ORAND/ORFuzzyValues

DOF

DefuzzifierDefuzzifier

Y1Y2

Yn…

Method of Defuzzification

Defuzzified output (real number)

Connectives

IF theta is POS AND theta_dot is POS THEN force is NEG

NEGZERO

POS

Fuzzifier(POS)

Fuzzifier(POS)

AND

DefuzzifierDefuzzifier

theta

theta_dot Fuzzifier(NEG)

Fuzzifier(NEG)

forceOutput Block

(NEG)

Output Block

(NEG)

Center of Gravity

Rule #2

Rule #3

force

force

Output

Change Detectors

Robot ObstacleRobot Obstacle AvoidanceAvoidance

Distance to obstacle (measured by sensor i)

Angle of sensor i w

v

Fuzzy-Logic-Controller

2

1

Fuzzy-Logic Obstacle AvoidanceFuzzy-Logic Obstacle Avoidance

a) Distance measured by sensor (d) b) Angle of sensor (th)

c) Forward velocity (V) d) Angular Velocity (W)

Close Near Far Neg Zero Pos

Zero VSlow Slow Fast Neg SNeg Zero SPos Pos

Membership Functions:

Fuzzy-Logic Obstacle AvoidanceFuzzy-Logic Obstacle Avoidance

1."IF distancei is Close And anglei is Pos Then V Is VSlow W is Neg "2."IF distancei is Close And anglei is Zero Then V Is Zero " 3."IF distancei is Close And anglei is Neg Then V Is VSlow W Is Pos "4."IF distancei is Near And anglei is Pos Then V Is Slow W Is SNeg"5."IF distancei is Near And anglei is Zero Then V Is VSlow W Is SNeg"6."IF distancei is Near And anglei is Neg Then V Is Slow W Is Spos"7."IF distancei is Far And anglei is Pos Then V Is Fast W Is Zero"8."IF distancei is Far And anglei is Zero Then V Is Fast W Is Zero"9."IF distancei is Far And anglei is Neg Then V Is Fast W Is Zero"

Fuzzy rules for the ith sensor:

Simulation timeSimulation time

Act

ivit

ies

Discrete Event FuzzyConventional Fuzzy

Conventional Fuzzy vs. DEVS-FuzzyConventional Fuzzy vs. DEVS-Fuzzy

Cooperative RoboticsCooperative Robotics

Cooperative pushing task:

Pushing task:Pushing task:

Pushing region

rArB

G��������������

B��������������

rw��������������

lw��������������

fr=0

fr<0fr>0

fl<0

fl=0fl>0

A

Cooperative pushing task:Cooperative pushing task:

rB

G��������������

B��������������

rw��������������lw

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Pushing regions

Cooperative Pushing TaskCooperative Pushing Task

“in”?command

“out”!goal_reached

“in”?object_reached

“in”?object_lost

“out”?object_touched

“in”?everybody_ready

“in”?outside_pushing_region

“out”?object_reached_the_goal

“in” “out”

Simulation ResultsSimulation Results

Simulation ResultsSimulation Results

Experiment ResultsExperiment Results

Experiment ResultsExperiment Results