Strategies for Cooperation Emergence in Distributed Service Discovery

Outline Discovery Strategy Promotion Techniques Results Conclusions

Strategies for Cooperation Emergencein Distributed Service Discovery

E. del Val M. Rebollo V. Botti

Univ. Politècnica de València (Spain)

COREDEMA ’13Salamanca, May 2013

M. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery


Promoting Cooperation

MotivationThere are scenarios in decentralized systems in which cooperationplays a central role

agents connected in networksbounded rationalityheterogeneous, self-interested agents



Our Proposal

The challengeObtain an emergent, cooperative global behavior even whencooperators are a minority, from local decisions.

What is done. . .a network structure that ensures navigation and efficiencystructural changes to isolate undesired agentsvariable incentives to promote cooperation



Outline

1 Outline

2 Discovery Strategy

3 Isolated Cooperation Promotion Techniques

4 Combined Cooperation Model

5 Results

6 Conclusions



Agent Network Model

A = {1, ..., n} a set of agents connected in aundirected network G , where N(i) denotes the neighbors ofagent ieach agent plays a role ri and offers a service si

agents have an initial behavior: cooperative (c) or notcooperative (nc)each agent has an initial budget b



Service Discovery

PurposeLocate in the network a similar enough service offer by a concreterole

qti = {stg , rtg ,TTL, ε, {}}

stg required semantic service descriptionrtg organizational role the target agent should playTTL: time to liveε similarity threshold in [0, 1]{} participant list (initially empty)



Discovery Process

iRi = {r1}Si = {s1}

kCH(k, t) = 0.5

jCH(j, t) = 0.5

nCH(n, t) = 0.15

A S R |N |k Sk Rk = {r1} 5n Sn Rn = {r2} 5j Sj Rj = {r1} 4

vRt = {r5}St = {s6}

mRm = {r7}Sm = {s7}

each agent knows itsdirect neighborsquery qt

i is redirected tothe most promisingneighbor



Similarity Measure

FNi(tg) = argmaxj∈Ni P(〈j , tg〉)

For each neighbor j , P(〈j , tg〉) determines the probability that theneighbor j redirects the search to the nearest network communitywhere there are more probabilities of finding the agent tg .

P(〈j , tg〉) = 1−

1−

CH(j , tg)∑k∈Ni

CH(k, tg)

kj



Social Plasticity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60

Pro

babi

lity

to m

aint

ain

the

link

Number of queries that were forwarded to other links

n = 2n = 4n = 6

rewiring action λ to avoidnon-cooperative agentsdecay function using asigmoidd parameter establishesbenevolence of the agent

Pdecay (rqij) =1

1+e−(rqij−d)

n



Social Plasticity Effects



Social Plasticity Effects



Incentives Effect



Social Plasticity and Incentives

When a neighbor j receives a query qti , it has a set of possible

actions Ac = {ρ,∞, 1, 2, ..., ki , ∅, λ}, where:

ρ is asking for a service∞ is providing the service{1, ..., ki} is forwarding the query to one of its neighbors ∈ Ni

∅ is doing nothingλ rewiring a link



Action Selection

Action Conditionat

i =∞ if |CH(i , tg)| ≥ εat

i = ∅ if |CH(i , tg)| < ε ∧at−1

j = ∅, j ∈ argmax(CHt−11 , ...,CHt−1

ki)

ati = j if |CH(i , tg)| < ε ∧

at−1j 6= 0, j ∈ argmax(CHt−1

1 , ...,CHt−1ki

)

ati = λ if at−1

i = j ∧at

j = ∅ ∧ |coop| < σ, coop ⊆ Ni(g)|j is a coop.



Action Selection

Action Conditionat

i =∞if |CH(i , tg)| ≥ ε

ati = ∅ if |CH(i , tg)| < ε ∧

at−1j = ∅, j ∈ argmax(CHt−1

1 , ...,CHt−1ki

)



1 , ...,CHt−1ki

)

ati = λ if at−1

i = j ∧at


Do the task if agent knows how to do it



Action Selection

Action Conditionat


i = ∅if |CH(i , tg)| < ε ∧

at−1j = ∅, j ∈ argmax(CHt−1

1 , ...,CHt−1ki

)



1 , ...,CHt−1ki

)

ati = λ if at−1

i = j ∧at


Do nothing if the agent guess that the most promising neighborwill no cooperate



Action Selection

Action Conditionat


i = ∅ if |CH(i , tg)| < ε ∧at−1

j = ∅, j ∈ argmax(CHt−11 , ...,CHt−1

ki)

ati = j

if |CH(i , tg)| < ε ∧


1 , ...,CHt−1ki

)

ati = λ if at−1

i = j ∧at


Forward the query if the agent guess that the most promisingneighbor will cooperate



Action Selection

Action Conditionat


i = ∅ if |CH(i , tg)| < ε ∧at−1

j = ∅, j ∈ argmax(CHt−11 , ...,CHt−1

ki)



1 , ...,CHt−1ki

)

ati = λ

if at−1i = j ∧

atj = ∅ ∧ |coop| < σ, coop ⊆ Ni(g)|j is a coop.

Rewire some links with a probability Pdecay if the agent issurrounded by non-coop agents



Costs of the Actions

uti (at

i ) =

−β if ati = ρ

p if ati =∞

−c if ati ∈ {1, 2, ..., ki}

0 if ati = ∅ ∧ @t ′ ≤ t : at′

i ∈ {1, 2, ...ki}α if at

i = ∅ ∧ ∃t ′ ≤ t : at′i ∈ {1, 2, ..., ki} ∧ ∃j ∈ A : at

j =∞−γ if at

i = λ



Incentives Policy

uniformly distributedSystem the system provides incentivesFixed the agent that request the service pays for it

base on a criterionPath depends on the length of the path

SimDg the more similar the higher rewardInvSimDg the less similar the higher reward



Experimental Parameters

network size: 1 000 agentsaverage degree of connection: 2.5similarity threshold ε = 0.75TTL = 100initial budget: 10040 % cooperative - 60 % non cooperative



Budget Distribution

Incentives

0

200

400

600

800

1000

1200

1400

1600

2 4 6 8 10 12 14 16 18 20

budget

degree of connection

Fixed Path Sim InvSim



Budget Distribution

Incentives

0

200

400

600

800

1000

1200

1400

1600

2 4 6 8 10 12 14 16 18 20

budget



Incentives + Social Plasticity

0

200

400

600

800

1000

1200

1400

1600

2 4 6 8 10 12 14 16 18 20

budget





Cooperative Behavior Rate

Incentives

0

200

400

600

800

1000

2 4 6 8 10 12 14 16 18

coop

snapshot

FixedPath

SimInvSim

System



Cooperative Behavior Rate

Incentives

0

200

400

600

800

1000

2 4 6 8 10 12 14 16 18

coop

snapshot

FixedPath

SimInvSim

System


0

200

400

600

800

1000

2 4 6 8 10 12 14 16 18

coop

snapshot

FixedPath

SimInvSim

System



Success Rate

Incentives

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18

%su

ccess

ful se

arc

hes

snapshot

Fixed Path Sim InvSim System



Success Rate

Incentives

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18

%su

ccess

ful se

arc

hes

snapshot



0

20

40

60

80

100

2 4 6 8 10 12 14 16 18

%su

ccess

ful se

arc

hes

snapshot

FixedPath

SimInvSim

System



Path Length

Incentives

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18

steps

snapshot




Path Length

Incentives

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18

steps

snapshot



0

20

40

60

80

100

2 4 6 8 10 12 14 16 18

steps

snapshot

FixedPath

SimInvSim

System



Num. of Broken Links (Rewired)

0

200

400

600

800

1000

2 4 6 8 10 12 14 16 18

bud

get

snapshot




Conclusions

What we’ve doneTo combine structural changes (social plasticity) with differentincentives policies in a decentralized service discovery scenario withlocal search.

What we’ve got

variable incentives work better than homogenous onescombination of mechanisms promotes cooperation in scenariosin which |nc| > |c|it increases the performance of the agents

reduces the average path lengthincreases the success rate


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Strategies for Cooperation Emergence in Distributed Service Discovery

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