On

the

Benefits

of

Cheating

by

Self-Interested

Agents

in

Vehicular

Networks∗

Raz

Lin

and

Sarit

Kraus

Computer

Science

Department

Bar-Ilan

University

Ramat-Gan,

Israel

{linraz,sarit}@cs.biu.ac.il

Yuval

Shavitt

School

of

Electrical

Engineering

Tel-Aviv

University,

Israel

shavitt@eng.tau.ac.il

ABSTRACT

As

more

and

more

cars

are

equipped

with

GPS

and

Wi-Fi

transmitters,

it

becomes

easier

to

design

systems

that

will

allow

cars

to

interact

autonomously

with

each

other,

e.g.,

regarding

traffic

on

the

roads.

Indeed,

car

manufacturers

are

already

equipping

their

cars

with

such

devices.

Though,

currently

these

systems

are

a

proprietary,

we

envision

a

natural

evolution

where

agent

applications

will

be

developed

for

vehicular

systems,

e.g.,

to

improve

car

routing

in

dense

urban

areas.

Nonetheless,

this

new

technology

and

agent

applications

may

lead

to

the

emergence

of

self-interested

car

owners,

who

will

care

more

about

their

own

welfare

than

the

social

welfare

of

their

peers.

These

car

owners

will

try

to

manipulate

their

agents

such

that

they

transmit

false

data

to

their

peers.

Using

a

simulation

environment,

which

models

a

real

transportation

network

in

a

large

city,

we

demonstrate

the

benefits

achieved

by

self-interested

agents

if

no

counter-measures

are

implemented.

Categories

and

Subject

Descriptors

[Artificial

Intelligence]:

Distributed

Artificial

Intelligence-Intelligent

agents

General

Terms

Experimentation

INTRODUCTION

As

technology

advances,

more

and

more

cars

are

being

equipped

with

devices,

which

enable

them

to

act

as

autonomous

agents.

An

important

advancement

in

this

respect

is

the

introduction

of

ad-hoc

communication

networks

(such

as

Wi-Fi),

which

enable

the

exchange

of

information

between

cars,

e.g.,

for

locating

road

congestions

and

optimal

routes

or

improving

traffic

safety

Vehicle-To-Vehicle

communication

is

already

onboard

by

some

car

manufactures,

enabling

the

collaboration

between

different

cars

on

the

road.

For

example,

GM"s

proprietary

algorithm

called

the

threat

assessment

algorithm,

constantly

calculates,

in

real

time,

other

vehicles"

positions

and

speeds,

and

enables

messaging

other

cars

when

a

collision

is

imminent;

Also,

Honda

has

began

testing

its

system

in

which

vehicles

talk

with

each

other

and

with

the

highway

system

itself

In

this

paper,

we

investigate

the

attraction

of

being

a

selfish

agent

in

vehicular

networks.

That

is,

we

investigate

the

benefits

achieved

by

car

owners,

who

tamper

with

on-board

devices

and

incorporate

their

own

self-interested

agents

in

them,

which

act

for

their

benefit.

We

build

on

the

notion

of

Gossip

Networks,

introduced

by

Shavitt

and

Shay

in

which

the

agents

can

obtain

road

congestion

information

by

gossiping

with

peer

agents

using

ad-hoc

communication.

We

recognize

two

typical

behaviors

that

the

self-interested

agents

could

embark

upon,

in

the

context

of

vehicular

networks.

In

the

first

behavior,

described

in

Section

the

objective

of

the

self-interested

agents

is

to

maximize

their

own

utility,

expressed

by

their

average

journey

duration

on

the

road.

This

situation

can

be

modeled

in

real

life

by

car

owners,

whose

aim

is

to

reach

their

destination

as

fast

as

possible,

and

would

like

to

have

their

way

free

of

other

cars.

To

this

end

they

will

let

their

agents

cheat

the

other

agents,

by

injecting

false

information

into

the

network.

This

is

achieved

by

reporting

heavy

traffic

values

for

the

roads

on

their

route

to

other

agents

in

the

network

in

the

hope

of

making

the

other

agents

believe

that

the

route

is

jammed,

and

causing

them

to

choose

a

different

route.

The

second

type

of

behavior,

described

in

Section

is

modeled

by

the

self-interested

agents"

objective

to

cause

disorder

in

the

network,

more

than

they

are

interested

in

maximizing

their

own

utility.

This

kind

of

behavior

could

be

generated,

for

example,

by

vandalism

or

terrorists,

who

aim

to

cause

as

much

mayhem

in

the

network

as

possible.

We

note

that

the

introduction

of

self-interested

agents

to

the

network,

would

most

probably

motivate

other

agents

to

try

and

detect

these

agents

in

order

to

minimize

their

effect.

This

is

similar,

though

in

a

different

context,

to

the

problem

introduced

by

Lamport

et

al.

as

the

Byzantine

Generals

Problem.

However,

the

introduction

of

mechanisms

to

deal

with

self-interested

agents

is

costly

and

time

consuming.

In

this

paper

we

focus

mainly

on

the

attractiveness

of

selfish

behavior

by

these

agents,

while

we

also

provide

some

insights

(RPS)

c

IFAAMAS

into

the

possibility

of

detecting

self-interested

agents

and

minimizing

their

effect.

To

demonstrate

the

benefits

achieved

by

self-interested

agents,

we

have

used

a

simulation

environment,

which

models

the

transportation

network

in

a

central

part

of

a

large

real

city.

The

simulation

environment

is

further

described

in

Section

Our

simulations

provide

insights

to

the

benefits

of

self-interested

agents

cheating.

Our

findings

can

motivate

future

research

in

this

field

in

order

to

minimize

the

effect

of

selfish-agents.

The

rest

of

this

paper

is

organized

as

follows.

In

Section

we

review

related

work

in

the

field

of

self-interested

agents

and

communications.

We

continue

and

formally

describe

our

environment

and

simulation

settings

in

Section

Sections

and

describe

the

different

behaviors

of

the

selfinterested

agents

and

our

findings.

Finally,

we

conclude

the

paper

with

open

questions

and

future

research

directions.

RELATED

WORK

In

their

seminal

paper,

Lamport

et

al.

describe

the

Byzantine

Generals

problem,

in

which

processors

need

to

handle

malfunctioning

components

that

give

conflicting

information

to

different

parts

of

the

system.

They

also

present

a

model

in

which

not

all

agents

are

connected,

and

thus

an

agent

cannot

send

a

message

to

all

the

other

agents.

Dolev

et

al.

has

built

on

this

problem

and

has

analyzed

the

number

of

faulty

agents

that

can

be

tolerated

in

order

to

eventually

reach

the

right

conclusion

about

true

data.

Similar

work

is

presented

by

Minsky

et

al.

who

discuss

techniques

for

constructing

gossip

protocols

that

are

resilient

to

up

to

t

malicious

host

failures.

As

opposed

to

the

above

works,

our

work

focuses

on

vehicular

networks,

in

which

the

agents

are

constantly

roaming

the

network

and

exchanging

data.

Also,

the

domain

of

transportation

networks

introduces

dynamic

data,

as

the

load

of

the

roads

is

subject

to

change.

In

addition,

the

system

in

transportation

networks

has

a

feedback

mechanism,

since

the

load

in

the

roads

depends

on

the

reports

and

the

movement

of

the

agents

themselves.

Malkhi

et

al.

present

a

gossip

algorithm

for

propagating

information

in

a

network

of

processors,

in

the

presence

of

malicious

parties.

Their

algorithm

prevents

the

spreading

of

spurious

gossip

and

diffuses

genuine

data.

This

is

done

in

time,

which

is

logarithmic

in

the

number

of

processes

and

linear

in

the

number

of

corrupt

parties.

Nevertheless,

their

work

assumes

that

the

network

is

static

and

also

that

the

agents

are

static

(they

discuss

a

network

of

processors).

This

is

not

true

for

transportation

networks.

For

example,

in

our

model,

agents

might

gossip

about

heavy

traffic

load

of

a

specific

road,

which

is

currently

jammed,

yet

this

information

might

be

false

several

minutes

later,

leaving

the

agents

to

speculate

whether

the

spreading

agents

are

indeed

malicious

or

not.

In

addition,

as

the

agents

are

constantly

moving,

each

agent

cannot

choose

with

whom

he

interacts

and

exchanges

data.

In

the

context

of

analyzing

the

data

and

deciding

whether

the

data

is

true

or

not,

researchers

have

focused

on

distributed

reputation

systems

or

decision

mechanisms

to

decide

whether

or

not

to

share

data.

Yu

and

Singh

build

a

social

network

of

agents"

reputations.

Every

agent

keeps

a

list

of

its

neighbors,

which

can

be

changed

over

time,

and

computes

the

trustworthiness

of

other

agents

by

updating

the

current

values

of

testimonies

obtained

from

reliable

referral

chains.

After

a

bad

experience

with

another

agent

every

agent

decreases

the

rating

of

the

"bad"

agent

and

propagates

this

bad

experience

throughout

the

network

so

that

other

agents

can

update

their

ratings

accordingly.

This

approach

might

be

implemented

in

our

domain

to

allow

gossip

agents

to

identify

self-interested

agents

and

thus

minimize

their

effect.

However,

the

implementation

of

such

a

mechanism

is

an

expensive

addition

to

the

infrastructure

of

autonomous

agents

in

transportation

networks.

This

is

mainly

due

to

the

dynamic

nature

of

the

list

of

neighbors

in

transportation

networks.

Thus,

not

only

does

it

require

maintaining

the

neighbors"

list,

since

the

neighbors

change

frequently,

but

it

is

also

harder

to

build

a

good

reputation

system.

Leckie

et

al.

focus

on

the

issue

of

when

to

share

information

between

the

agents

in

the

network.

Their

domain

involves

monitoring

distributed

sensors.

Each

agent

monitors

a

subset

of

the

sensors

and

evaluates

a

hypothesis

based

on

the

local

measurements

of

its

sensors.

If

the

agent

believes

that

a

hypothesis

is

sufficient

likely

he

exchanges

this

information

with

the

other

agents.

In

their

domain,

the

goal

of

all

the

agents

is

to

reach

a

global

consensus

about

the

likelihood

of

the

hypothesis.

In

our

domain,

however,

as

the

agents

constantly

move,

they

have

many

samples,

which

they

exchange

with

each

other.

Also,

the

data

might

also

vary

(e.g.,

a

road

might

be

reported

as

jammed,

but

a

few

minutes

later

it

could

be

free),

thus

making

it

harder

to

decide

whether

to

trust

the

agent,

who

sent

the

data.

Moreover,

the

agent

might

lie

only

about

a

subset

of

its

samples,

thus

making

it

even

harder

to

detect

his

cheating.

Some

work

has

been

done

in

the

context

of

gossip

networks

or

transportation

networks

regarding

the

spreading

of

data

and

its

dissemination.

Datta

et

al.

focus

on

information

dissemination

in

mobile

ad-hoc

networks

(MANET).

They

propose

an

autonomous

gossiping

algorithm

for

an

infrastructure-less

mobile

ad-hoc

networking

environment.

Their

autonomous

gossiping

algorithm

uses

a

greedy

mechanism

to

spread

data

items

in

the

network.

The

data

items

are

spread

to

immediate

neighbors

that

are

interested

in

the

information,

and

avoid

ones

that

are

not

interested.

The

decision

which

node

is

interested

in

the

information

is

made

by

the

data

item

itself,

using

heuristics.

However,

their

work

concentrates

on

the

movement

of

the

data

itself,

and

not

on

the

agents

who

propagate

the

data.

This

is

different

from

our

scenario

in

which

each

agent

maintains

the

data

it

has

gathered,

while

the

agent

itself

roams

the

road

and

is

responsible

(and

has

the

capabilities)

for

spreading

the

data

to

other

agents

in

the

network.

Das

et

al.

propose

a

cooperative

strategy

for

content

delivery

in

vehicular

networks.

In

their

domain,

peers

download

a

file

from

a

mesh

and

exchange

pieces

of

the

file

among

themselves.

We,

on

the

other

hand,

are

interested

in

vehicular

networks

in

which

there

is

no

rule

forcing

the

agents

to

cooperate

among

themselves.

Shibata

et

al.

propose

a

method

for

cars

to

cooperatively

and

autonomously

collect

traffic

jam

statistics

to

estimate

arrival

time

to

destinations

for

each

car.

The

communication

is

based

on

IEEE

without

using

a

fixed

infrastructure

on

the

ground.

While

we

use

the

same

domain,

we

focus

on

a

different

problem.

Shibata

et

al.

mainly

focus

on

efficiently

broadcasting

the

data

between

agents

(e.g.,

avoid

duplicates

and

communication

overhead),

as

we

focus

on

the

case

where

agents

are

not

cooperative

in

The

Sixth

Intl.

Joint

Conf.

on

Autonomous

Agents

and

Multi-Agent

Systems

(AAMAS

nature,

and

on

how

selfish

agents

affect

other

agents

and

the

network

load.

Wang

et

al.

also

assert,

in

the

context

of

wireless

networks,

that

individual

agents

are

likely

to

do

what

is

most

beneficial

for

their

owners,

and

will

act

selfishly.

They

design

a

protocol

for

communication

in

networks

in

which

all

agents

are

selfish.

Their

protocol

motivates

every

agent

to

maximize

its

profit

only

when

it

behaves

truthfully

(a

mechanism

of

incentive

compatibility).

However,

the

domain

of

wireless

networks

is

quite

different

from

the

domain

of

transportation

networks.

In

the

wireless

network,

the

wireless

terminal

is

required

to

contribute

its

local

resources

to

transmit

data.

Thus,

Wang

et

al.

use

a

payment

mechanism,

which

attaches

costs

to

terminals

when

transmitting

data,

and

thus

enables

them

to

maximize

their

utility

when

transmitting

data,

instead

of

acting

selfishly.

Unlike

this,

in

the

context

of

transportation

networks,

constructing

such

a

mechanism

is

not

quite

a

straightforward

task,

as

self-interested

agents

and

regular

gossip

agents

might

incur

the

same

cost

when

transmitting

data.

The

difference

between

the

two

types

of

agents

only

exists

regarding

the

credibility

of

the

data

they

exchange.

In

the

next

section,

we

will

describe

our

transportation

network

model

and

gossiping

between

the

agents.

We

will

also

describe

the

different

agents

in

our

system.

MODEL

AND

SIMULATIONS

We

first

describe

the

formal

transportation

network

model,

and

then

we

describe

the

simulations

designs.

Formal

Model

Following

Shavitt

and

Shay

and

Parshani

the

transportation

network

is

represented

by

a

directed

graph

G(V,

E),

where

V

is

the

set

of

vertices

representing

junctions,

and

E

is

the

set

of

edges,

representing

roads.

An

edge

e

E

is

associated

with

a

weight

w

which

specifies

the

time

it

takes

to

traverse

the

road

associated

with

that

edge.

The

roads"

weights

vary

in

time

according

to

the

network

(traffic)

load.

Each

car,

which

is

associated

with

an

autonomous

agent,

is

given

a

pair

of

origin

and

destination

points

(vertices).

A

journey

is

defined

as

the

(not

necessarily

simple)

path

taken

by

an

agent

between

the

origin

vertex

and

the

destination

vertex.

We

assume

that

there

is

always

a

path

between

a

source

and

a

destination.

A

journey

length

is

defined

as

the

sum

of

all

weights

of

the

edges

constituting

this

path.

Every

agent

has

to

travel

between

its

origin

and

destination

points

and

aims

to

minimize

its

journey

length.

Initially,

agents

are

ignorant

about

the

state

of

the

roads.

Regular

agents

are

only

capable

of

gathering

information

about

the

roads

as

they

traverse

them.

However,

we

assume

that

some

agents

have

means

of

inter-vehicle

communication

(e.g.,

IEEE

with

a

given

communication

range,

which

enables

them

to

communicate

with

other

agents

with

the

same

device.

Those

agents

are

referred

to

as

gossip

agents.

Since

the

communication

range

is

limited,

the

exchange

of

information

using

gossiping

is

done

in

one

of

two

ways:

(a)

between

gossip

agents

passing

one

another,

or

(b)

between

gossip

agents

located

at

the

same

junction.

We

assume

that

each

agent

stores

the

most

recent

information

it

has

received

or

gathered

around

the

edges

in

the

network.

A

subset

of

the

gossip

agents

are

those

agents

who

are

selfinterested

and

manipulate

the

devices

for

their

own

benefit.

We

will

refer

to

these

agents

as

self-interested

agents.

A

detailed

description

of

their

behavior

is

given

in

Sections

and

Simulation

Design

Building

on

the

network

in

our

simulations

replicates

a

central

part

of

a

large

city,

and

consists

of

junctions

and

roads,

which

are

approximately

the

number

of

main

streets

in

the

city.

Each

simulation

consists

of

iterations.

The

basic

time

unit

of

the

iteration

is

a

step,

which

equivalents

to

about

seconds.

Each

iteration

simulates

six

hours

of

movements.

The

average

number

of

cars

passing

through

the

network

during

the

iteration

is

about

and

the

average

number

of

cars

in

the

network

at

a

specific

time

unit

is

about

cars.

In

each

iteration

the

same

agents

are

used

with

the

same

origin

and

destination

points,

whereas

the

data

collected

in

earlier

iterations

is

preserved

in

the

future

iterations

(referred

to

as

the

history

of

the

agent).

This

allows

us

to

simulate

somewhat

a

daily

routine

in

the

transportation

network

(e.g.,

a

working

week).

Each

of

the

experiments

that

we

describe

below

is

run

with

different

traffic

scenarios.

Each

such

traffic

scenario

differs

from

one

another

by

the

initial

load

of

the

roads

and

the

designated

routes

of

the

agents

(cars)

in

the

network.

For

each

such

scenario

simulations

are

run,

creating

a

total

of

simulations

for

each

experiment.

It

has

been

shown

by

Parshani

et

al.

that

the

information

propagation

in

the

network

is

very

efficient

when

the

percentage

of

gossiping

agents

is

or

more.

Yet,

due

to

congestion

caused

by

too

many

cars

rushing

to

what

is

reported

as

the

less

congested

part

of

the

network

of

gossiping

agents

leads

to

the

most

efficient

routing

results

in

their

experiments.

Thus,

in

our

simulation,

we

focus

only

on

simulations

in

which

the

percentage

of

gossip

agents

is

The

simulations

were

done

with

different

percentages

of

self-interested

agents.

To

gain

statistical

significance

we

ran

each

simulation

with

changes

in

the

set

of

the

gossip

agents,

and

the

set

of

the

self-interested

agents.

In

order

to

gain

a

similar

ordinal

scale,

the

results

were

normalized.

The

normalized

values

were

calculated

by

comparing

each

agent"s

result

to

his

results

when

the

same

scenario

was

run

with

no

self-interested

agents.

This

was

done

for

all

of

the

iterations.

Using

the

normalized

values

enabled

us

to

see

how

worse

(or

better)

each

agent

would

perform

compared

to

the

basic

setting.

For

example,

if

an

average

journey

length

of

a

certain

agent

in

iteration

with

no

selfinterested

agent

was

and

the

length

was

in

the

same

scenario

and

iteration

in

which

self-interested

agents

were

involved,

then

the

normalized

value

for

that

agent

would

be

More

details

regarding

the

simulations

are

described

in

Sections

and

SPREADING

LIES,

MAXIMIZING

UTILITY

In

the

first

set

of

experiments

we

investigated

the

benefits

achieved

by

the

self-interested

agents,

whose

aim

was

to

minimize

their

own

journey

length.

The

self-interested

agents

adopted

a

cheating

approach,

in

which

they

sent

false

data

to

their

peers.

In

this

section

we

first

describe

the

simulations

with

the

self-interested

agents.

Then,

we

model

the

scenario

as

a

The

Sixth

Intl.

Joint

Conf.

on

Autonomous

Agents

and

Multi-Agent

Systems

(AAMAS

game

with

two

types

of

agents,

and

prove

that

the

equilibrium

result

can

only

be

achieved

when

there

is

no

efficient

exchange

of

gossiping

information

in

the

network.

Modeling

the

Self-Interested

Agents"

Behavior

While

the

gossip

agents

gather

data

and

send

it

to

other

agents,

the

self-interested

agents"

behavior

is

modeled

as

follows:

Calculate

the

shortest

path

from

origin

to

destination.

Communicate

the

following

data

to

other

agents:

(a)

If

the

road

is

not

in

the

agent"s

route

send

the

true

data

about

it

(e.g.,

data

about

roads

it

has

received

from

other

agents)

(b)

For

all

roads

in

the

agent"s

route,

which

the

agent

has

not

yet

traversed,

send

a

random

high

weight.

Basically,

the

self-interested

agent

acts

the

same

as

the

gossip

agent.

It

collects

data

regarding

the

weight

of

the

roads

(either

by

traversing

the

road

or

by

getting

the

data

from

other

agents)

and

sends

the

data

it

has

collected

to

other

agents.

However,

the

self-interested

agent

acts

differently

when

the

road

is

in

its

route.

Since

the

agent"s

goal

is

to

reach

its

destination

as

fast

as

possible,

the

agent

will

falsely

report

that

all

the

roads

in

its

route

are

heavily

congested.

This

is

in

order

to

free

the

path

for

itself,

by

making

other

agents

recalculate

their

paths,

this

time

without

including

roads

on

the

self-interested

agent"s

route.

To

this

end,

for

all

the

roads

in

its

route,

which

the

agent

has

not

yet

passed,

the

agent

generates

a

random

weight,

which

is

above

the

average

weight

of

the

roads

in

the

network.

It

then

associates

these

new

weights

with

the

roads

in

its

route

and

sends

them

to

the

other

agents.

While

an

agent

can

also

divert

cars

from

its

route

by

falsely

reporting

congested

roads

in

parallel

to

its

route

as

free,

this

behavior

is

not

very

likely

since

other

agents,

attempting

to

use

the

roads,

will

find

the

mistake

within

a

short

time

and

spread

the

true

congestion

on

the

road.

On

the

other

hand,

if

an

agent

manages

to

persuade

other

agents

not

to

use

a

road,

it

will

be

harder

for

them

to

detect

that

the

said

roads

are

not

congested.

In

addition,

to

avoid

being

influenced

by

its

own

lies

and

other

lies

spreading

in

the

network,

all

self-interested

agents

will

ignore

data

received

about

roads

with

heavy

traffic

(note

that

data

about

roads

that

are

not

heavily

traffic

will

not

be

In

the

next

subsection

we

describe

the

simulation

results,

involving

the

self-interested

agents.

Simulation

Results

To

test

the

benefits

of

cheating

by

the

self-interested

agents

we

ran

several

experiments.

In

the

first

set

of

experiments,

we

created

a

scenario,

in

which

a

small

group

of

self-interested

agents

spread

lies

on

the

same

route,

and

tested

its

effect

on

the

journey

length

of

all

the

agents

in

the

network.

In

other

simulations

we

have

run,

in

which

there

had

been

several

real

congestions

in

the

network,

we

indeed

saw

that

even

when

the

roads

are

jammed,

the

self-interested

agents

were

less

affected

if

they

ignored

all

reported

heavy

traffic,

since

by

such

they

also

discarded

all

lies

roaming

the

network

Table

Normalized

journey

length

values,

selfinterested

agents

with

the

same

route

Iteration

Self-Interested

Gossip

Gossip

Regular

Number

Agents

SR

Others

Agents

Thus,

several

cars,

which

had

the

same

origin

and

destination

points,

were

designated

as

self-interested

agents.

In

this

simulation,

we

selected

only

agents

to

be

part

of

the

group

of

the

self-interested

agents,

as

we

wanted

to

investigate

the

effect

achieved

by

only

a

small

number

of

agents.

In

each

simulation

in

this

experiment,

different

agents

were

randomly

chosen

to

be

part

of

the

group

of

self-interested

agents,

as

described

above.

In

addition,

one

road,

on

the

route

of

these

agents,

was

randomly

selected

to

be

partially

blocked,

letting

only

one

car

go

through

that

road

at

each

time

step.

About

agents

were

randomly

selected

as

regular

gossip

agents,

and

the

other

agents

were

designated

as

regular

agents.

We

analyzed

the

average

journey

length

of

the

self-interested

agents

as

opposed

to

the

average

journey

length

of

other

regular

gossip

agents

traveling

along

the

same

route.

Table

summarizes

the

normalized

results

for

the

self-interested

agents,

the

gossip

agents

(those

having

the

same

origin

and

destination

points

as

the

self-interested

agents,

denoted

Gossip

SR,

and

all

other

gossip

agents,

denoted

Gossip

Others)

and

the

regular

agents,

as

a

function

of

the

iteration

number.

We

can

see

from

the

results

that

the

first

time

the

selfinterested

agents

traveled

the

route

while

spreading

the

false

data

about

the

roads

did

not

help

them

(using

the

paired

t-test

we

show

that

those

agents

had

significantly

lower

journey

lengths

in

the

scenario

in

which

they

did

not

spread

any

lies,

with

p

This

is

mainly

due

to

the

fact

that

the

lies

do

not

bypass

the

self-interested

agent

and

reach

other

cars

that

are

ahead

of

the

self-interested

car

on

the

same

route.

Thus,

spreading

the

lies

in

the

first

iteration

does

not

help

the

self-interested

agent

to

free

the

route

he

is

about

to

travel,

in

the

first

iteration.

Only

when

the

self-interested

agents

had

repeated

their

journey

in

the

next

iteration

(iteration

did

it

help

them

significantly

(p

The

reason

for

this

is

that

other

gossip

agents

received

this

data

and

used

it

to

recalculate

their

shortest

path,

thus

avoiding

entrance

to

the

roads,

for

which

the

self-interested

agents

had

spread

false

information

about

congestion.

It

is

also

interesting

to

note

the

large

value

attained

by

the

self-interested

agents

in

the

first

iteration.

This

is

mainly

due

to

several

self-interested

agents,

who

entered

the

jammed

road.

This

situation

occurred

since

the

self-interested

agents

ignored

all

heavy

traffic

data,

and

thus

ignored

the

fact

that

the

road

was

jammed.

As

they

started

spreading

lies

about

this

road,

more

cars

shifted

from

this

route,

thus

making

the

road

free

for

the

future

iterations.

However,

we

also

recall

that

the

self-interested

agents

ignore

all

information

about

the

heavy

traffic

roads.

Thus,

The

Sixth

Intl.

Joint

Conf.

on

Autonomous

Agents

and

Multi-Agent

Systems

(AAMAS

Table

Normalized

journey

length

values,

spreading

lies

for

a

beneficiary

agent

Iteration

Beneficiary

Gossip

Gossip

Regular

Number

Agent

SR

Others

Agents

when

the

network

becomes

congested,

more

self-interested

cars

are

affected,

since

they

might

enter

jammed

roads,

which

they

would

otherwise

not

have

entered.

This

can

be

seen,

for

example,

in

iterations

in

which

the

normalized

value

of

the

self-interested

agents

increased

above

Using

the

paired

t-test

to

compare

these

values

with

the

values

achieved

by

these

agents

when

no

lies

are

used,

we

see

that

there

is

no

significant

difference

between

the

two

scenarios.

As

opposed

to

the

gossip

agents,

we

can

see

how

little

effect

the

self-interested

agents

have

on

the

regular

agents.

As

compared

to

the

gossip

agents

on

the

same

route

that

have

traveled

as

much

as

more,

when

self-interested

agents

are

introduced,

the

average

journey

length

for

the

regular

agents

has

only

increased

by

about

This

result

is

even

lower

than

the

effect

on

other

gossip

agents

in

the

entire

network.

Since

we

noticed

that

cheating

by

the

self-interested

agents

does

not

benefit

them

in

the

first

iteration,

we

devised

another

set

of

experiments.

In

the

second

set

of

experiments,

the

self-interested

agents

have

the

objective

to

help

another

agent,

who

is

supposed

to

enter

the

network

some

time

after

the

self-interested

agent

entered.

We

refer

to

the

latter

agent

as

the

beneficiary

agent.

Just

like

a

self-interested

agent,

the

beneficiary

agent

also

ignores

all

data

regarding

heavy

traffic.

In

real-life

this

can

be

modeled,

for

example,

by

a

husband,

who

would

like

to

help

his

wife

find

a

faster

route

to

her

destination.

Table

summarizes

the

normalized

values

for

the

different

agents.

As

in

the

first

set

of

experiments,

simulations

were

run

for

each

scenario,

with

a

total

of

simulations.

In

each

of

these

simulation

one

agent

was

randomly

selected

as

a

self-interested

agent,

and

then

another

agent,

with

the

same

origin

as

the

selfinterested

agent,

was

randomly

selected

as

the

beneficiary

agent.

The

other

and

agents

were

designated

as

regular

gossip

agents

and

regular

agents,

respectively.

We

can

see

that

as

the

number

of

iterations

advances,

the

lower

the

normalized

value

for

the

beneficiary

agent.

In

this

scenario,

just

like

the

previous

one,

in

the

first

iterations,

not

only

does

the

beneficiary

agent

not

avoid

the

jammed

roads,

since

he

ignores

all

heavy

traffic,

he

also

does

not

benefit

from

the

lies

spread

by

the

self-interested

agent.

This

is

due

to

the

fact

that

the

lies

are

not

yet

incorporated

by

other

gossip

agents.

Thus,

if

we

compare

the

average

journey

length

in

the

first

iteration

when

lies

are

spread

and

when

there

are

no

lies,

the

average

is

significantly

lower

when

there

are

no

lies

(p

On

the

other

hand,

if

we

compare

the

average

journey

length

in

all

of

the

iterations,

there

is

no

significant

difference

between

the

two

settings.

Still,

in

most

of

the

iterations,

the

average

journey

length

of

the

beneficiary

agent

is

longer

than

in

the

case

when

no

lies

are

spread.

We

can

also

see

the

impact

on

the

other

agents

in

the

system.

While

the

gossip

agents,

which

are

not

on

the

route

of

the

beneficiary

agent,

virtually

are

not

affected

by

the

self-interested

agent,

those

on

the

route

and

the

regular

agents

are

affected

and

have

higher

normalized

values.

That

is,

even

with

just

one

self-interested

car,

we

can

see

that

both

the

gossip

agents

that

follow

the

same

route

as

the

lies

spread

by

the

self-interested

agents,

and

other

regular

agents,

increase

their

journey

length

by

more

than

In

our

third

set

of

experiments

we

examined

a

setting

in

which

there

was

an

increasing

number

of

agents,

and

the

agents

did

not

necessarily

have

the

same

origin

and

destination

points.

To

model

this

we

randomly

selected

selfinterested

agents,

whose

objective

was

to

minimize

their

average

journey

length,

assuming

the

cars

were

repeating

their

journeys

(that

is,

more

than

one

iteration

was

made).

As

opposed

to

the

first

set

of

experiments,

in

this

set

the

self-interested

agents

were

selected

randomly,

and

we

did

not

enforce

the

constraint

that

they

will

all

have

the

same

origin

and

destination

points.

As

in

the

previous

sets

of

experiments

we

ran

different

simulations

per

scenario.

In

each

simulation

runs

were

made,

each

run

with

different

numbers

of

self-interested

agents:

(no

self-interested

agents),

and

Each

agent

adopted

the

behavior

modeled

in

Section

Figure

shows

the

normalized

value

achieved

by

the

self-interested

agents

as

a

function

of

their

number.

The

figure

shows

these

values

for

iterations

The

first

iteration

is

not

shown

intentionally,

as

we

assume

repeated

journeys.

Also,

we

have

seen

in

the

previous

set

of

experiments

and

we

have

provided

explanations

as

to

why

the

self-interested

agents

do

not

gain

much

from

their

behavior

in

the

first

iteration.

Self-Interested

Agents

Number

NormalizedValue

Iteration

Iteration

Iteration

Iteration

Iteration

Figure

Self-interested

agents

normalized

values

as

a

function

of

the

number

of

self-interested

agents.

Using

these

simulations

we

examined

what

the

threshold

could

be

for

the

number

of

randomly

selected

self-interested

agents

in

order

to

allow

themselves

to

benefit

from

their

selfish

behavior.

We

can

see

that

up

to

self-interested

agents,

the

average

normalized

value

is

below

That

is,

they

benefit

from

their

malicious

behavior.

In

the

case

of

one

self-interested

agent

there

is

a

significant

difference

between

the

average

journey

length

of

when

the

agent

spread

lies

and

when

no

lies

are

spread

(p

while

when

The

Sixth

Intl.

Joint

Conf.

on

Autonomous

Agents

and

Multi-Agent

Systems

(AAMAS

there

are

and

self-interested

agents

there

is

no

significance

difference.

Yet,

as

the

number

of

self-interested

agents

increases,

the

normalized

value

also

increases.

In

such

cases,

the

normalized

value

is

larger

than

and

the

self-interested

agents

journey

length

becomes

significantly

higher

than

their

journey

length,

in

cases

where

there

are

no

self-interested

agents

in

the

system.

In

the

next

subsection

we

analyze

the

scenario

as

a

game

and

show

that

when

in

equilibrium

the

exchange

of

gossiping

between

the

agents

becomes

inefficient.

When

Gossiping

is

Inefficient

We

continued

and

modeled

our

scenario

as

a

game,

in

order

to

find

the

equilibrium.

There

are

two

possible

types

for

the

agents:

(a)

regular

gossip

agents,

and

(b)

self-interested

agents.

Each

of

these

agents

is

a

representative

of

its

group,

and

thus

all

agents

in

the

same

group

have

similar

behavior.

We

note

that

the

advantage

of

using

gossiping

in

transportation

networks

is

to

allow

the

agents

to

detect

anomalies

in

the

network

(e.g.,

traffic

jams)

and

to

quickly

adapt

to

them

by

recalculating

their

routes

We

also

assume

that

the

objective

of

the

self-interested

agents

is

to

minimize

their

own

journey

length,

thus

they

spread

lies

on

their

routes,

as

described

in

Section

We

also

assume

that

sophisticated

methods

for

identifying

the

self-interested

agents

or

managing

reputation

are

not

used.

This

is

mainly

due

to

the

complexity

of

incorporating

and

maintaining

such

mechanisms,

as

well

as

due

to

the

dynamics

of

the

network,

in

which

interactions

between

different

agents

are

frequent,

agents

may

leave

the

network,

and

data

about

the

road

might

change

as

time

progresses

(e.g.,

a

road

might

be

reported

by

a

regular

gossip

agent

as

free

at

a

given

time,

yet

it

may

currently

be

jammed

due

to

heavy

traffic

on

the

road).

Let

Tavg

be

the

average

time

it

takes

to

traverse

an

edge

in

the

transportation

network

(that

is,

the

average

load

of

an

edge).

Let

Tmax

be

the

maximum

time

it

takes

to

traverse

an

edge.

We

will

investigate

the

game,

in

which

the

self-interested

and

the

regular

gossip

agents

can

choose

the

following

actions.

The

self-interested

agents

can

choose

how

much

to

lie,

that

is,

they

can

choose

to

spread

how

long

(not

necessarily

the

true

duration)

it

takes

to

traverse

certain

roads.

Since

the

objective

of

the

self-interested

agents

is

to

spread

messages

as

though

some

roads

are

jammed,

the

traversal

time

they

report

is

obviously

larger

than

the

average

time.

We

denote

the

time

the

self-interested

agents

spread

as

Ts,

such

that

Tavg

Ts

Tmax.

Motivated

by

the

results

of

the

simulations

we

have

described

above,

we

saw

that

the

agents

are

less

affected

if

they

discard

the

heavy

traffic

values.

Thus,

the

regular

gossip

cars,

attempting

to

mitigate

the

effect

of

the

liars,

can

choose

a

strategy

to

ignore

abnormal

congestion

values

above

a

certain

threshold,

Tg.

Obviously,

Tavg

Tg

Tmax.

In

order

to

prevent

the

gossip

agents

from

detecting

the

lies

and

just

discarding

those

values,

the

self-interested

agents

send

lies

in

a

given

range,

[Ts,

Tmax],

with

an

inverse

geometric

distribution,

that

is,

the

higher

the

T

value,

the

higher

its

frequency.

Now

we

construct

the

utility

functions

for

each

type

of

agents,

which

is

defined

by

the

values

of

Ts

and

Tg.

If

the

self-interested

agents

spread

traversal

times

higher

than

or

equal

to

the

regular

gossip

cars"

threshold,

they

will

not

benefit

from

those

lies.

Thus,

the

utility

value

of

the

selfinterested

agents

in

this

case

is

On

the

other

hand,

if

the

self-interested

agents

spread

traversal

time

which

is

lower

than

the

threshold,

they

will

gain

a

positive

utility

value.

From

the

regular

gossip

agents

point-of-view,

if

they

accept

messages

from

the

self-interested

agents,

then

they

incorporate

the

lies

in

their

calculation,

thus

they

will

lose

utility

points.

On

the

other

hand,

if

they

discard

the

false

values

the

self-interested

agents

send,

that

is,

they

do

not

incorporate

the

lies,

they

will

gain

utility

values.

Formally,

we

use

us

to

denote

the

utility

of

the

self-interested

agents

and

ug

to

denote

the

utility

of

the

regular

gossip

agents.

We

also

denote

the

strategy

profile

in

the

game

as

{Ts,

Tg}.

The

utility

functions

are

defined

as:

us

if

Ts

Tg

Ts

Tavg

if

Ts

Tg

ug

Tg

Tavg

if

Ts

Tg

Ts

Tg

if

Ts

Tg

We

are

interested

in

finding

the

Nash

equilibrium.

We

recall

from

that

the

Nash

equilibrium

is

a

strategy

profile,

where

no

player

has

anything

to

gain

by

deviating

from

his

strategy,

given

that

the

other

agent

follows

his

strategy

profile.

Formally,

let

(S,

u)

denote

the

game,

where

S

is

the

set

of

strategy

profiles

and

u

is

the

set

of

utility

functions.

When

each

agent

i

{regular

gossip,

self-interested}

chooses

a

strategy

Ti

resulting

in

a

strategy

profile

T

(Ts,

Tg)

then

agent

i

obtains

a

utility

of

ui

(T).

A

strategy

profile

T∗

S

is

a

Nash

equilibrium

if

no

deviation

in

the

strategy

by

any

single

agent

is

profitable,

that

is,

if

for

all

i,

ui

(T∗

ui

(Ti,

T∗

−i).

That

is,

(Ts,

Tg)

is

a

Nash

equilibrium

if

the

self-interested

agents

have

no

other

value

Ts

such

that

us

(Ts,

Tg)

us

(Ts,

Tg),

and

similarly

for

the

gossip

agents.

We

now

have

the

following

theorem.

Theorem

(Tavg,

Tavg)

is

the

only

Nash

equilibrium.

Proof.

First

we

will

show

that

(Tavg,

Tavg)

is

a

Nash

equilibrium.

Assume,

by

contradiction,

that

the

gossip

agents

choose

another

value

Tg

Tavg.

Thus,

ug

(Tavg,

Tg

Tavg

Tg

On

the

other

hand,

ug

(Tavg,

Tavg)

Thus,

the

regular

gossip

agents

have

no

incentive

to

deviate

from

this

strategy.

The

self-interested

agents

also

have

no

incentive

to

deviate

from

this

strategy.

By

contradiction,

again

assume

that

the

self-interested

agents

choose

another

value

Ts

Tavg.

Thus,

us

(Ts

Tavg)

while

us

(Tavg,

Tavg)

We

will

now

show

that

the

above

solution

is

unique.

We

will

show

that

any

other

tuple

(Ts,

Tg),

such

that

Tavg

Tg

Tmax

and

Tavg

Ts

Tmax

is

not

a

Nash

equilibrium.

We

have

three

cases.

In

the

first

Tavg

Tg

Ts

Tmax.

Thus,

us

(Ts,

Tg)

and

ug

(Ts,

Tg)

Tg

Tavg.

In

this

case,

the

regular

gossip

agents

have

an

incentive

to

deviate

and

choose

another

strategy

Tg

since

by

doing

so

they

increase

their

own

utility:

ug

(Ts,

Tg

Tg

Tavg.

In

the

second

case

we

have

Tavg

Ts

Tg

Tmax.

Thus,

ug

(Ts,

Tg)

Ts

Tg

Also,

the

regular

gossip

agents

have

an

incentive

to

deviate

and

choose

another

strategy

Tg

in

which

their

utility

value

is

higher:

ug

(Ts,

Tg

Ts

Tg

In

the

last

case

we

have

Tavg

Ts

Tg

Tmax.

Thus,

us

(Ts,

Tg)

Ts

Tg

In

this

case,

the

self-interested

agents

have

an

incentive

to

deviate

and

choose

another

strategy

Tg

in

which

their

utility

value

is

higher:

us

(Tg

Tg)

Tg

Tavg

Tg

Tavg

The

Sixth

Intl.

Joint

Conf.

on

Autonomous

Agents

and

Multi-Agent

Systems

(AAMAS

Table

Normalized

journey

length

values

for

the

first

iteration

Self-Interested

Self-Interested

Gossip

Regular

Agents

Number

Agents

Agents

Agents

Table

Normalized

journey

length

values

for

all

iterations

Self-Interested

Self-Interested

Gossip

Regular

Agents

Number

Agents

Agents

Agents

The

above

theorem

proves

that

the

equilibrium

point

is

reached

only

when

the

self-interested

agents

send

the

time

to

traverse

certain

edges

equals

the

average

time,

and

on

the

other

hand

the

regular

gossip

agents

discard

all

data

regarding

roads

that

are

associated

with

an

average

time

or

higher.

Thus,

for

this

equilibrium

point

the

exchange

of

gossiping

information

between

agents

is

inefficient,

as

the

gossip

agents

are

unable

to

detect

any

anomalies

in

the

network.

In

the

next

section

we

describe

another

scenario

for

the

self-interested

agents,

in

which

they

are

not

concerned

with

their

own

utility,

but

rather

interested

in

maximizing

the

average

journey

length

of

other

gossip

agents.

SPREADING

LIES,

CAUSING

CHAOS

Another

possible

behavior

that

can

be

adopted

by

selfinterested

agents

is

characterized

by

their

goal

to

cause

disorder

in

the

network.

This

can

be

achieved,

for

example,

by

maximizing

the

average

journey

length

of

all

agents,

even

at

the

cost

of

maximizing

their

own

journey

length.

To

understand

the

vulnerability

of

the

gossip

based

transportation

support

system,

we

ran

different

simulations

for

each

scenario.

In

each

simulation

different

agents

were

randomly

chosen

(using

a

uniform

distribution)

to

act

as

gossip

agents,

among

them

self-interested

agents

were

chosen.

Each

self-interested

agent

behaved

in

the

same

manner

as

described

in

Section

Every

simulation

consisted

of

runs

with

each

run

comprising

different

numbers

of

self-interested

agents:

(no

selfinterested

agents),

and

Also,

in

each

run

the

number

of

self-interested

agents

was

increased

incrementally.

For

example:

the

run

with

selfinterested

agents

consisted

of

all

the

self-interested

agents

that

were

used

in

the

run

with

self-interested

agents,

but

with

an

additional

self-interested

agents.

Tables

and

summarize

the

normalized

journey

length

for

the

self-interested

agents,

the

regular

gossip

agents

and

the

regular

(non-gossip)

agents.

Table

summarizes

the

data

for

the

first

iteration

and

Table

summarizes

the

data

for

the

average

of

all

iterations.

Figure

demonstrates

the

changes

in

the

normalized

values

for

the

regular

gossip

agents

and

the

regular

agents,

as

a

function

of

the

iteration

number.

Similar

to

the

results

in

our

first

set

of

experiments,

described

in

Section

we

can

see

that

randomly

selected

self-interested

agents

who

follow

different

randomly

selected

routes

do

not

benefit

from

their

malicious

behavior

(that

is,

their

average

journey

length

does

not

decrease).

However,

when

only

one

self-interested

agent

is

involved,

it

does

benefit

from

the

malicious

behavior,

even

in

the

first

iteration.

The

results

also

indicate

that

the

regular

gossip

agents

are

more

sensitive

to

malicious

behavior

than

regular

agentsthe

average

journey

length

for

the

gossip

agents

increases

significantly

(e.g.,

with

self-interested

agents

the

average

journey

length

for

the

gossip

agents

was

higher

than

in

the

setting

with

no

self-interested

agents

at

all,

as

opposed

to

an

increase

of

only

for

the

regular

agents).

In

contrast,

these

results

also

indicate

that

the

self-interested

agents

do

not

succeed

in

causing

a

significant

load

in

the

network

by

their

malicious

behavior.

Iteration

Number

NormalizedValue

self-interested

agents,

gossip

agents

normalized

value

self-interested

agents,

gossip

agents

normalized

value

self-interested

agents,

regular

agents

normalized

value

self-interested

agents,

regular

agents

normalized

value

Figure

Gossip

and

regular

agents

normalized

values,

as

a

function

of

the

iteration.

Since

the

goal

of

the

self-interested

agents

in

this

case

is

to

cause

disorder

in

the

network

rather

than

use

the

lies

for

their

own

benefits,

the

question

arises

as

to

why

would

the

behavior

of

the

self-interested

agents

be

to

send

lies

about

their

routes

only.

Furthermore,

we

hypothesize

that

if

they

all

send

lies

about

the

same

major

roads

the

damage

they

might

inflict

on

the

entire

network

would

be

larger

that

had

each

of

them

sent

lies

about

its

own

route.

To

examine

this

hypothesis,

we

designed

another

set

of

experiments.

In

this

set

of

experiments,

all

the

self-interested

agents

spread

lies

about

the

same

main

roads

in

the

network.

However,

the

results

show

quite

a

smaller

impact

on

other

gossip

and

reguThe

Sixth

Intl.

Joint

Conf.

on

Autonomous

Agents

and

Multi-Agent

Systems

(AAMAS

Table

Normalized

journey

length

values

for

all

iterations.

Network

with

congestions.

Self-Interested

Self-Interested

Gossip

Regular

Agents

Number

Agents

Agents

Agents

lar

agents

in

the

network.

The

average

normalized

value

for

the

gossip

agents

in

these

simulations

was

only

about

as

opposed

to

in

the

original

scenario.

When

analyzing

the

results

we

saw

that

although

the

false

data

was

spread,

it

did

not

cause

other

gossip

cars

to

change

their

route.

The

main

reason

was

that

the

lies

were

spread

on

roads

that

were

not

on

the

route

of

the

self-interested

agents.

Thus,

it

took

the

data

longer

to

reach

agents

on

the

main

roads,

and

when

the

agents

reached

the

relevant

roads

this

data

was

too

old

to

be

incorporated

in

the

other

agents

calculations.

We

also

examined

the

impact

of

sending

lies

in

order

to

cause

chaos

when

there

are

already

congestions

in

the

network.

To

this

end,

we

simulated

a

network

in

which

main

roads

are

jammed.

The

behavior

of

the

self-interested

agents

is

as

described

in

Section

and

the

self-interested

agents

spread

lies

about

their

own

route.

The

simulation

results,

detailed

in

Table

show

that

there

is

a

greater

incentive

for

the

self-interested

agents

to

cheat

when

the

network

is

already

congested,

as

their

cheating

causes

more

damage

to

the

other

agents

in

the

network.

For

example,

whereas

the

average

journey

length

of

the

regular

agents

increased

only

by

about

in

the

original

scenario,

in

which

the

network

was

not

congested,

in

this

scenario

the

average

journey

length

of

the

agents

had

increased

by

about

CONCLUSIONS

In

this

paper

we

investigated

the

benefits

achieved

by

self-interested

agents

in

vehicular

networks.

Using

simulations

we

investigated

two

behaviors

that

might

be

taken

by

self-interested

agents:

(a)

trying

to

minimize

their

journey

length,

and

(b)

trying

to

cause

chaos

in

the

network.

Our

simulations

indicate

that

in

both

behaviors

the

selfinterested

agents

have

only

limited

success

achieving

their

goal,

even

if

no

counter-measures

are

taken.

This

is

in

contrast

to

the

greater

impact

inflicted

by

self-interested

agents

in

other

domains

(e.g.,

E-Commerce).

Some

reasons

for

this

are

the

special

characteristics

of

vehicular

networks

and

their

dynamic

nature.

While

the

self-interested

agents

spread

lies,

they

cannot

choose

which

agents

with

whom

they

will

interact.

Also,

by

the

time

their

lies

reach

other

agents,

they

might

become

irrelevant,

as

more

recent

data

has

reached

the

same

agents.

Motivated

by

the

simulation

results,

future

research

in

this

field

will

focus

on

modeling

different

behaviors

of

the

self-interested

agents,

which

might

cause

more

damage

to

the

network.

Another

research

direction

would

be

to

find

ways

of

minimizing

the

effect

of

selfish-agents

by

using

distributed

reputation

or

other

measures.

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M.

M.

Hugue

(Eds.).

IEEE

Computer

Society

Press,

C.

Leckie

and

R.

Kotagiri.

Policies

for

sharing

distributed

probabilistic

beliefs.

In

Proceedings

of

ACSC,

pages

Adelaide,

Australia,

D.

Malkhi,

E.

Pavlov,

and

Y.

Sella.

Gossip

with

malicious

parties.

Technical

report:

School

of

Computer

Science

and

Engineering

The

Hebrew

University

of

Jerusalem,

Israel,

March

Y.

M.

Minsky

and

F.

B.

Schneider.

Tolerating

malicious

gossip.

Distributed

Computing,

February

M.

J.

Osborne

and

A.

Rubinstein.

A

Course

In

Game

Theory.

MIT

Press,

Cambridge

MA,

R.

Parshani.

Routing

in

gossip

networks.

Master"s

thesis,

Department

of

Computer

Science,

Bar-Ilan

University,

Ramat-Gan,

Israel,

October

R.

Parshani,

S.

Kraus,

and

Y.

Shavitt.

A

study

of

gossiping

in

transportation

networks.

Submitted

for

publication,

Y.

Shavitt

and

A.

Shay.

Optimal

routing

in

gossip

networks.

IEEE

Transactions

on

Vehicular

Technology,

July

N.

Shibata,

T.

Terauchi,

T.

Kitani,

K.

Yasumoto,

M.

Ito,

and

T.

Higashino.

A

method

for

sharing

traffic

jam

information

using

inter-vehicle

communication.

In

Proceedings

of

USA,

W.

Wang,

X.-Y.

Li,

and

Y.

Wang.

Truthful

multicast

routing

in

selfish

wireless

networks.

In

Proceedings

of

MobiCom,

pages

USA,

B.

Yu

and

M.

P.

Singh.

A

social

mechanism

of

reputation

management

in

electronic

communities.

In

Proceedings

of

CIA,

The

Sixth

Intl.

Joint

Conf.

on

Autonomous

Agents

and

Multi-Agent

Systems

(AAMAS

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

self-interested agent
vehicular network
intelligent agent
agent-base deploy application
artificial social system

social network
journey length
chaos
selfinterested agent