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-12TRANSCRIPT
Efficient Opinion Sharingin Large Decentralised Teams
Oleksandr Pryymak, Alex Rogers and Nicholas R. Jennings{op08r,acr,nrj}@ecs.soton.ac.uk
University of SouthamptonAgents, Interaction andComplexity Group
6 June 2012AAMAS'12
Disaster response and Large 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
Disaster response and Large 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
Challenge
How to improve the accuracy of shared
opinions?
Opinion Sharing Model Networked team
Opinions are introduced gradually
Noisy
Weights (levels of importance) define sharing process
Agents' model
Agents' model
Agents' model
Dynamics of the Opinion Sharing
Stable Transition Unstable
Stable Dynamics
Unstable Dynamics
Transition
Dynamics of the Opinion Sharing
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)
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
AAT: sample run
AAT: stages
Executes 3 stages by each agent:
Select candidate importance levels
Estimate the awareness rates they deliver
Select the best one to use
However, the agent's choice highly influences others
AAT: Candidate Importance Levels
This 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.
AAT:Estimation of the Awareness Rates
Awareness 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
AAT:Strategy to Choose an Importance Level
Since an agent's 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.
Results: Target Awareness Rate
Compromise awareness for overall Reliability
Results: Target Awareness Rate
Compromise awareness for overall Reliability
Results: Reliability and Convergence
Random Network
Results: Reliability and Convergence
Scale-free Network
Results: Reliability and Convergence
Small-world Network
Results: Communication Expenses
Minimal Communication = #messages to share an opinion in a single cascade (total #neighbours)
Results: Indifferent AgentsWhat if some of the agents cannot alter their weights?
Summary
Presented 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?