Strategies for Cooperation Emergence in Distributed Service Discovery
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Strategies for Cooperation Emergence in Distributed Service Discovery

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Talk in Workshop on Conflict Resolution in Decision Making (COREDEMA '13)

Talk in Workshop on Conflict Resolution in Decision Making (COREDEMA '13)

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Strategies for Cooperation Emergence in Distributed Service Discovery Strategies for Cooperation Emergence in Distributed Service Discovery Presentation Transcript

  • Outline Discovery Strategy Promotion Techniques Results ConclusionsStrategies for Cooperation Emergencein Distributed Service DiscoveryE. del Val M. Rebollo V. BottiUniv. Politècnica de València (Spain)COREDEMA ’13Salamanca, May 2013M. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsPromoting CooperationMotivationThere are scenarios in decentralized systems in which cooperationplays a central roleagents connected in networksbounded rationalityheterogeneous, self-interested agentsM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsOur ProposalThe 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 cooperationM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsOutline1 Outline2 Discovery Strategy3 Isolated Cooperation Promotion Techniques4 Combined Cooperation Model5 Results6 ConclusionsM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsAgent Network ModelA = {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 siagents have an initial behavior: cooperative (c) or notcooperative (nc)each agent has an initial budget bM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsService DiscoveryPurposeLocate in the network a similar enough service offer by a concreteroleqti = {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)M. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsDiscovery ProcessiRi = {r1}Si = {s1}kCH(k, t) = 0.5jCH(j, t) = 0.5nCH(n, t) = 0.15A S R |N|k Sk Rk = {r1} 5n Sn Rn = {r2} 5j Sj Rj = {r1} 4vRt = {r5}St = {s6}mRm = {r7}Sm = {s7}each agent knows itsdirect neighborsquery qti is redirected tothe most promisingneighborM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsSimilarity MeasureFNi (tg) = argmaxj∈NiP( 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∈NiCH(k, tg)kjM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsSocial Plasticity00.10.20.30.40.50.60.70.80.910 10 20 30 40 50 60ProbabilitytomaintainthelinkNumber of queries that were forwarded to other linksn = 2n = 4n = 6rewiring action λ to avoidnon-cooperative agentsdecay function using asigmoidd parameter establishesbenevolence of the agentPdecay (rqij) = 11+e−(rqij −d)nM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsSocial Plasticity EffectsM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsSocial Plasticity EffectsM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsIncentives EffectM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsSocial Plasticity and IncentivesWhen a neighbor j receives a query qti , it has a set of possibleactions 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 linkM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsAction SelectionAction Conditionati = ∞ if |CH(i, tg)| ≥ εati = ∅ if |CH(i, tg)| < ε ∧at−1j = ∅, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = j if |CH(i, tg)| < ε ∧at−1j = 0, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = λ if at−1i = j ∧atj = ∅ ∧ |coop| < σ, coop ⊆ Ni (g)|j is a coop.M. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsAction SelectionAction Conditionati = ∞if |CH(i, tg)| ≥ εati = ∅ if |CH(i, tg)| < ε ∧at−1j = ∅, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = j if |CH(i, tg)| < ε ∧at−1j = 0, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = λ if at−1i = j ∧atj = ∅ ∧ |coop| < σ, coop ⊆ Ni (g)|j is a coop.Do the task if agent knows how to do itM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsAction SelectionAction Conditionati = ∞ if |CH(i, tg)| ≥ εati = ∅if |CH(i, tg)| < ε ∧at−1j = ∅, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = j if |CH(i, tg)| < ε ∧at−1j = 0, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = λ if at−1i = j ∧atj = ∅ ∧ |coop| < σ, coop ⊆ Ni (g)|j is a coop.Do nothing if the agent guess that the most promising neighborwill no cooperateM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsAction SelectionAction Conditionati = ∞ if |CH(i, tg)| ≥ εati = ∅ if |CH(i, tg)| < ε ∧at−1j = ∅, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = jif |CH(i, tg)| < ε ∧at−1j = 0, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = λ if at−1i = j ∧atj = ∅ ∧ |coop| < σ, coop ⊆ Ni (g)|j is a coop.Forward the query if the agent guess that the most promisingneighbor will cooperateM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsAction SelectionAction Conditionati = ∞ if |CH(i, tg)| ≥ εati = ∅ if |CH(i, tg)| < ε ∧at−1j = ∅, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = j if |CH(i, tg)| < ε ∧at−1j = 0, j ∈ argmax(CHt−11 , ..., CHt−1ki)ati = λif 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 agentsM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsCosts of the Actionsuti (ati ) =−β if ati = ρp if ati = ∞−c if ati ∈ {1, 2, ..., ki }0 if ati = ∅ ∧ t ≤ t : ati ∈ {1, 2, ...ki }α if ati = ∅ ∧ ∃t ≤ t : ati ∈ {1, 2, ..., ki } ∧ ∃j ∈ A : atj = ∞−γ if ati = λM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsIncentives Policyuniformly distributedSystem the system provides incentivesFixed the agent that request the service pays for itbase on a criterionPath depends on the length of the pathSimDg the more similar the higher rewardInvSimDg the less similar the higher rewardM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsExperimental Parametersnetwork size: 1 000 agentsaverage degree of connection: 2.5similarity threshold ε = 0.75TTL = 100initial budget: 10040 % cooperative - 60 % non cooperativeM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsBudget DistributionIncentives020040060080010001200140016002 4 6 8 10 12 14 16 18 20budgetdegree of connectionFixed Path Sim InvSimM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsBudget DistributionIncentives020040060080010001200140016002 4 6 8 10 12 14 16 18 20budgetdegree of connectionFixed Path Sim InvSimIncentives + Social Plasticity020040060080010001200140016002 4 6 8 10 12 14 16 18 20budget degree of connectionFixed Path Sim InvSimM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsCooperative Behavior RateIncentives020040060080010002 4 6 8 10 12 14 16 18coopsnapshotFixedPathSimInvSimSystemM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsCooperative Behavior RateIncentives020040060080010002 4 6 8 10 12 14 16 18coopsnapshotFixedPathSimInvSimSystemIncentives + Social Plasticity020040060080010002 4 6 8 10 12 14 16 18coop snapshotFixedPathSimInvSimSystemM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsSuccess RateIncentives0204060801002 4 6 8 10 12 14 16 18%successfulsearchessnapshotFixed Path Sim InvSim SystemM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsSuccess RateIncentives0204060801002 4 6 8 10 12 14 16 18%successfulsearchessnapshotFixed Path Sim InvSim SystemIncentives + Social Plasticity0204060801002 4 6 8 10 12 14 16 18%successfulsearches snapshotFixedPathSimInvSimSystemM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsPath LengthIncentives0204060801002 4 6 8 10 12 14 16 18stepssnapshotFixed Path Sim InvSim SystemM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsPath LengthIncentives0204060801002 4 6 8 10 12 14 16 18stepssnapshotFixed Path Sim InvSim SystemIncentives + Social Plasticity0204060801002 4 6 8 10 12 14 16 18steps snapshotFixedPathSimInvSimSystemM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsNum. of Broken Links (Rewired)020040060080010002 4 6 8 10 12 14 16 18budgetsnapshotFixed Path Sim InvSim SystemM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery
  • Outline Discovery Strategy Promotion Techniques Results ConclusionsConclusionsWhat we’ve doneTo combine structural changes (social plasticity) with differentincentives policies in a decentralized service discovery scenario withlocal search.What we’ve gotvariable incentives work better than homogenous onescombination of mechanisms promotes cooperation in scenariosin which |nc| > |c|it increases the performance of the agentsreduces the average path lengthincreases the success rateM. Rebollo et al. (UPV) COREDEMA’13Strategies for Cooperation Emergence in Distributed Service Discovery