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Efficient opinion sharing in large decentralised teams
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Efficient opinion sharing in large decentralised teams


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This is a version without animation of the system dynamics, unfortunately it is not supported by any on-line tools. Presented on AAMAS-12

This is a version without animation of the system dynamics, unfortunately it is not supported by any on-line tools. Presented on AAMAS-12

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  • 1. Efficient Opinion Sharingin Large Decentralised Teams Oleksandr Pryymak, Alex Rogers and Nicholas R. Jennings {op08r,acr,nrj} University of Southampton Agents, Interaction and Complexity Group 6 June 2012 AAMAS12
  • 2. Disaster response andLarge Decentralised Teams 2010, Haiti earthquake Citizen and public news reporting (Ushahidi) 2010, Chile earthquake "Twitter is one of the speediest, albeit not the most accurate, sources of real-time information" France24
  • 3. Disaster response andLarge Decentralised Teams  Teams are large  Decentralised  Few opinion sources  Observations are uncertain and conflicting  Agents share only opinions without supporting information (Communication is strictly limited) Opinion is a subjective belief about the common subject of interest
  • 4. Challenge How to improve the accuracy of shared opinions?
  • 5. Opinion Sharing Model  Networked team  Opinions are introduced gradually  Noisy  Weights (levels of importance) define sharing process
  • 6. Agents model
  • 7. Agents model
  • 8. Agents model
  • 9. Dynamics of the Opinion SharingStable Transition Unstable
  • 10. Stable Dynamics
  • 11. Unstable Dynamics
  • 12. Transition
  • 13. Dynamics of the Opinion Sharing
  • 14. Problem How to find the settings for improved reliability? Requirements: − Decentralised − On-line − Adaptive (i.e. complex topology, size, degree) − Minimise communication DACOR algorithm Distributed Adaptive Communication for Overall Reliability by R. Glinton, P. Scerri, and K. Sycara − introduces excessive communication overhead (#neighbours2) − exhibits low adaptivity (3 parameters to tune)
  • 15. Autonomous Adaptive Tuning (AAT) Finds tcritical for each agent individually Each agent must use the minimal importance level that still enables it to form its opinion
  • 16. AAT: sample run
  • 17. AAT: stagesExecutes 3 stages by each agent: Select candidate importance levels Estimate the awareness rates they deliver Select the best one to useHowever, the agents choice highly influences others
  • 18. AAT: Candidate Importance LevelsThis stage limits the search space.Initialise an agent once with candidates: drawn from the range with a given step size. However, the algorithm becomes computationally expensive that lead to opinion formation on different update steps. Thus, the agent exhibits different dynamics.
  • 19. AAT:Estimation of the Awareness RatesAwareness Rate is a probability of forming an opinion with a given importance level.2 evidences indicate that agent could have formed an opinion with a given candidate: If an opinion was formed, then all higher levels would have led to opinion formation Otherwise, a candidate requires less updates to form an opinion than was observed
  • 20. AAT:Strategy to Choose an Importance LevelSince an agents choice influences others, strategies with less dramatic changes to the dynamics perform better Hill-climbing: Select the importance level which is closest to the currently used (with the awareness rate closest to the target) Outperforms popular MAB strategies.
  • 21. Results: Target Awareness RateCompromise awareness for overall Reliability
  • 22. Results: Target Awareness RateCompromise awareness for overall Reliability
  • 23. Results: Reliability and Convergence Random Network
  • 24. Results: Reliability and Convergence Scale-free Network
  • 25. Results: Reliability and Convergence Small-world Network
  • 26. Results: Communication Expenses Minimal Communication = #messages to share an opinion in a single cascade (total #neighbours)
  • 27. Results: Indifferent AgentsWhat if some of the agents cannot alter their weights?
  • 28. SummaryPresented a novel algorithm, AAT, that: − improves the reliability of the opinions − outperforms the existing algorithm, DACOR, and prediction of the best setting (Av.Pre-tuned) − the first that minimises communication to opinion sharing only − Computationally inexpensive − Adaptive, scalable and robust to the presence of indifferent agents − Operates without a knowledge of the context and the ground truth What to take away?