This document presents an Autonomous Adaptive Tuning (AAT) algorithm for improving the accuracy of shared opinions in large, decentralized teams. AAT finds the optimal importance level for each agent to use in sharing opinions in an online, distributed manner that minimizes communication costs. Evaluation results show AAT improves opinion reliability over existing algorithms and approaches, works across different network structures, and remains effective even when some agents cannot change their importance levels. The key contribution is a novel method for decentralized, adaptive tuning of importance levels for reliable opinion sharing in large teams with limited communication.

1. Efficient Opinion Sharing
in Large Decentralised Teams
Oleksandr Pryymak, Alex Rogers
and Nicholas R. Jennings
{op08r,acr,nrj}@ecs.soton.ac.uk
University of Southampton
Agents, Interaction and
Complexity Group
6 June 2012
AAMAS'12

2. 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

3. 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

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 Sharing
Stable 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: 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

18. 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.

19. 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

20. 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.

21. Results: Target Awareness Rate
Compromise awareness for overall Reliability

22. Results: Target Awareness Rate
Compromise 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 Agents
What if some of the agents cannot alter their weights?

28. 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?