presentation given on IJCAI-HINA 2011 workshop http://bit.ly/HINA2011 the paper itself: http://eprints.ecs.soton.ac.uk/22435/

1. Introduction Model AAT Experiments and Results Conclusions
Efficient Sharing of Conflicting Opinions with
Minimal Communication
in Large Decentralised Teams
Oleksandr Pryymak, Alex Rogers and Nicholas R. Jennings
University of Southampton
{op08r,acr,nrj}@ecs.soton.ac.uk
July 20, 2011
0 / 19

2. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
1 / 19

3. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
2010, Chile earthquake – Twitter is one of
the speediest, albeit not the most accurate,
sources of real-time information (France24).
1 / 19

4. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
2010, Chile earthquake – Twitter is one of
the speediest, albeit not the most accurate,
sources of real-time information (France24).
Large teams of individuals
1 / 19

5. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
2010, Chile earthquake – Twitter is one of
the speediest, albeit not the most accurate,
sources of real-time information (France24).
Large teams of individuals
Decentralised
1 / 19

6. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
2010, Chile earthquake – Twitter is one of
the speediest, albeit not the most accurate,
sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make an
observation
1 / 19

7. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
2010, Chile earthquake – Twitter is one of
the speediest, albeit not the most accurate,
sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make an
observation
Observations are uncertain and conflicting
1 / 19

8. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
2010, Chile earthquake – Twitter is one of
the speediest, albeit not the most accurate,
sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make an
observation
Observations are uncertain and conflicting
Individuals share opinions without
supporting information
1 / 19

9. Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and public
news reporting, plotted on an online map
(Ushahidi).
2010, Chile earthquake – Twitter is one of
the speediest, albeit not the most accurate,
sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make an
observation
Observations are uncertain and conflicting
Individuals share opinions without
supporting information
How opinions are shared and how to improve their accuracy?
1 / 19

10. Introduction Model AAT Experiments and Results Conclusions
How opinions are shared – Can we trust what we share?
2 / 19

11. Introduction Model AAT Experiments and Results Conclusions
How opinions are shared – Can we trust what we share?
Opinions are shared in cascades (avalanches)
2 / 19

12. Introduction Model AAT Experiments and Results Conclusions
How opinions are shared – Can we trust what we share?
Can we trust what we share?
Opinions are shared in cascades (avalanches)
2 / 19

13. Introduction Model AAT Experiments and Results Conclusions
How opinions are shared – Can we trust what we share?
Can we trust what we share?
Chile’10 : yes / no (Mendoza et al.
2010)
Opinions are shared in cascades (avalanches)
2 / 19

14. Introduction Model AAT Experiments and Results Conclusions
How opinions are shared – Can we trust what we share?
Can we trust what we share?
Chile’10 : yes / no (Mendoza et al.
2010)
Santiago airport is closed
Fire at the University of
Conceptcion
Looting in Conceptcion
Opinions are shared in cascades (avalanches)
2 / 19

15. Introduction Model AAT Experiments and Results Conclusions
How opinions are shared – Can we trust what we share?
Can we trust what we share?
Chile’10 : yes / no (Mendoza et al.
2010)
Santiago airport is closed
Fire at the University of
Conceptcion
Looting in Conceptcion
Looting in Santiago
Tsunami warning
Active volcano
Opinions are shared in cascades (avalanches)
2 / 19

16. Introduction Model AAT Experiments and Results Conclusions
How opinions are shared – Can we trust what we share?
Can we trust what we share?
Chile’10 : yes / no (Mendoza et al.
2010)
Santiago airport is closed
Fire at the University of
Conceptcion
Looting in Conceptcion
Looting in Santiago
Tsunami warning
Active volcano
Opinions are shared in cascades (avalanches)
Even in cooperative settings opinions might be incorrect
2 / 19

17. Introduction Model AAT Experiments and Results Conclusions
Problem of Forming a Correct Opinion
How do agents make a decision which opinion is correct?
based on own priors, observations
based on information from others
3 / 19

18. Introduction Model AAT Experiments and Results Conclusions
Problem of Forming a Correct Opinion
How do agents make a decision which opinion is correct?
based on own priors, observations
based on information from others
by analysing communicated information
reaching agreements interactivity with others
3 / 19

19. Introduction Model AAT Experiments and Results Conclusions
Problem of Forming a Correct Opinion
How do agents make a decision which opinion is correct?
based on own priors, observations
based on information from others
by analysing communicated information
reaching agreements interactivity with others
The Problem
However, if:
agents’ processing abilities are limited
communication is strictly limited to opinion sharing
3 / 19

20. Introduction Model AAT Experiments and Results Conclusions
Problem of Forming a Correct Opinion
How do agents make a decision which opinion is correct?
based on own priors, observations
based on information from others
by analysing communicated information
reaching agreements interactivity with others
The Problem
However, if:
agents’ processing abilities are limited
communication is strictly limited to opinion sharing
The Solution
Agents have to exploit properties of opinion sharing dynamics, and
filter out incorrect opinions in the sharing process
3 / 19

21. Introduction Model AAT Experiments and Results Conclusions
Problem of Forming a Correct Opinion
How do agents make a decision which opinion is correct?
based on own priors, observations
based on information from others
by analysing communicated information
reaching agreements interactivity with others
The Problem
However, if:
agents’ processing abilities are limited
communication is strictly limited to opinion sharing
The Solution
Agents have to exploit properties of opinion sharing dynamics, and
filter out incorrect opinions in the sharing process
How to find such settings by independent actions of the agents?
3 / 19

22. Introduction Model AAT Experiments and Results Conclusions
Outline
Remaining sections:
1 Model of opinion sharing
2 Existing message-passing algorithm
3 Our algorithm based on independent actions
4 Evaluation
4 / 19

23. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Agent
5 / 19

24. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
Agent
5 / 19

25. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
5 / 19

26. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
5 / 19

27. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
Belief
0 Prior 1
5 / 19

28. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
Belief
0 Prior 1
Updated with:
Own observations
sensors
5 / 19

29. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
Belief
0 Prior 1
Updated with:
Own observations
sensors
5 / 19

30. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
Belief
0 Prior 1
Updated with:
Own observations
sensors
Opinions' of others network
...
Yes ? No No neighbours
5 / 19

31. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
Belief
0 Prior 1
Updated with:
Own observations
sensors
Opinions' of others network
...
Yes ? No No neighbours
5 / 19

32. Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain Subject of
tonight? interest
No Don't know Yes Opinion
Agent
Belief
0 Prior 1
Updated with:
Own observations
sensors
Opinions' of others network
...
Yes ? No No neighbours
5 / 19

33. Introduction Model AAT Experiments and Results Conclusions
Model – Sample Dynamics
red nodes are agents with sensors;
green nodes are agents with undeter. opinion;
white and black are agents that support the
corresponding opinions. (b = white)
6 / 19

34. Introduction Model AAT Experiments and Results Conclusions
Model – Sample Dynamics
opinions are shared in
cascades
red nodes are agents with sensors;
green nodes are agents with undeter. opinion;
white and black are agents that support the
corresponding opinions. (b = white)
6 / 19

35. Introduction Model AAT Experiments and Results Conclusions
Model – Sample Dynamics
opinions are shared in
cascades
cascades might be
wrong and fragile
red nodes are agents with sensors;
green nodes are agents with undeter. opinion;
white and black are agents that support the
corresponding opinions. (b = white)
6 / 19

36. Introduction Model AAT Experiments and Results Conclusions
Model – Sample Dynamics
opinions are shared in
cascades
cascades might be
wrong and fragile
cascades depend on
trust levels
red nodes are agents with sensors;
green nodes are agents with undeter. opinion;
white and black are agents that support the
corresponding opinions. (b = white)
6 / 19

37. Introduction Model AAT Experiments and Results Conclusions
Model – Sample Dynamics
opinions are shared in
cascades
cascades might be
wrong and fragile
cascades depend on
trust levels
double counting fallacy
red nodes are agents with sensors;
green nodes are agents with undeter. opinion;
white and black are agents that support the
corresponding opinions. (b = white)
6 / 19

38. Introduction Model AAT Experiments and Results Conclusions
Settings for Improved Reliability – Metrics
100
Agents holding opinion, %
80
60 correct
incorrect
40 undetermined
20
0
0.55 0.6 tcritical 0.65 0.7 0.75
Stable dynamics Scale-Invariant dynamics Unstable dynamics
1
0.8
Reliability
0.6
Reliability
0.4 Awareness
0.2
0
0.55 0.6 t
critical 0.65 0.7 0.75
Trust level (common for all agents) 7 / 19

39. Introduction Model AAT Experiments and Results Conclusions
Cascades Distribution
Stable Dynamics Scale-Invariant Dynamics Unstable Dynamics
4
t=0.6 4
t=0.63 2
t=0.66
10 10 10
3 3
10 10
Cascade Frequency
Cascade Frequency
Cascade Frequency
2 2 1
10 10 10
1 1
10 10
0 0 0
10 10 10
0 1 2 3 0 1 2 3 0 1 2 3
10 10 10 10 10 10 10 10 10 10 10 10
Size of Opinion Cascade Size of Opinion Cascade Size of Opinion Cascade
8 / 19

40. Introduction Model AAT Experiments and Results Conclusions
Cascades Distribution
Stable Dynamics Scale-Invariant Dynamics Unstable Dynamics
4
t=0.6 4
t=0.63 2
t=0.66
10 10 10
3 3
10 10
Cascade Frequency
Cascade Frequency
Cascade Frequency
2 2 1
10 10 10
1 1
10 10
0 0 0
10 10 10
0 1 2 3 0 1 2 3 0 1 2 3
10 10 10 10 10 10 10 10 10 10 10 10
Size of Opinion Cascade Size of Opinion Cascade Size of Opinion Cascade
Branching factor of opinion sharing
αimproved reliability = 1
R. Glinton, P. Scerri, and K. Sycara. (2010)
Exploiting scale invariant dynamics for efficient information propagation in large teams.
In Proceedings of 9th International Conference on Autonomous Agents and Multiagent Systems
(AAMAS’10), pages 21-28, Toronto, Canada.
8 / 19

41. Introduction Model AAT Experiments and Results Conclusions
DACOR
Yes
? ?
α
Yes
?
?
introduces additional communication
NumberOfNeighbours 2 additional messages for a single
opinion change
9 / 19

42. Introduction Model AAT Experiments and Results Conclusions
DACOR
Yes
? ?
α
Yes
?
?
introduces additional communication
NumberOfNeighbours 2 additional messages for a single
opinion change
exhibits low adaptivity
requires tuning of its parameters
9 / 19

43. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on local
observations only?
10 / 19

44. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on local
observations only?
Stable dynamics Scale-Invariant dynamics Unstable dynamics
1
0.8
Reliability
0.6
Reliability
0.4 Awareness
0.2
0
0.55 0.6 tcritical 0.65 0.7 0.75
Trust level (common for all agents)
10 / 19

45. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on local
observations only?
Stable dynamics Scale-Invariant dynamics Unstable dynamics
1
0.8
Reliability
0.6
Reliability
0.4 Awareness
0.2
0
0.55 0.6 tcritical 0.65 0.7 0.75
Trust level (common for all agents)
Intuition
An agent must use the minimal trust level that still enables it to
form its opinion
10 / 19

46. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on local
observations only?
Stable dynamics Scale-Invariant dynamics Unstable dynamics
1
0.8
Reliability
0.6
Reliability
0.4 Awareness
0.2
0
0.55 0.6 tcritical 0.65 0.7 0.75
Trust level (common for all agents)
Intuition
An agent must use the minimal trust level that still enables it to
form its opinion
However, the agent’s choice influences others in the team
10 / 19

47. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level til from the candidates.
11 / 19

48. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level til from the candidates.
The agent with til has to achieve the target awareness rate, hbest
11 / 19

49. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level til from the candidates.
The agent with til has to achieve the target awareness rate, hbest
ti = arg min |hi (til ) − hbest |
til
11 / 19

50. Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level til from the candidates.
The agent with til has to achieve the target awareness rate, hbest
ti = arg min |hi (til ) − hbest |
til
1 How to select candidate trust levels?
2 How to estimate their awareness rates?
3 How to choose the trust level to use?
11 / 19

51. Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
ck te
la hi
k=3 =b k=1
2 =w
oi oi
Pki
0 1-σ P'i σ 1
To form the most accurate opinion the agent must form its opinion
when it observes the strongest support.
12 / 19

52. Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
ck te
la hi
k=1 =b k= 1 = w
oi oi
1+
1− ti 2−
2+
ti
ti ti
P k
i Pki
0 1-σ P'i 0.5 σ 1 0 1-σ P'i 0.5 σ 1
To form the most accurate opinion the agent must form its opinion
when it observes the strongest support.
12 / 19

53. Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
ck te
la hi
k=1 =b k= 1 = w
oi oi
1+
1− ti 2−
2+
ti
ti ti
P k
i Pki
0 1-σ P'i 0.5 σ 1 0 1-σ P'i 0.5 σ 1
To form the most accurate opinion the agent must form its opinion
when it observes the strongest support.
Since the number of neighbours |Ni | is limited, the set of the
candidate trust levels is:
Ti = {til− , til+ : l = 1 . . . |Ni |}
12 / 19

54. Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
ck te
la hi
k=1 =b k= 1 = w
oi oi
1+
1− ti 2−
2+
ti
ti ti
P k
i Pki
0 1-σ P'i 0.5 σ 1 0 1-σ P'i 0.5 σ 1
To form the most accurate opinion the agent must form its opinion
when it observes the strongest support.
Since the number of neighbours |Ni | is limited, the set of the
candidate trust levels is:
Ti = {til− , til+ : l = 1 . . . |Ni |}
In the settings of dynamic topology and agent may use arbitrary Ti
12 / 19

55. Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot be
calculated.
13 / 19

56. Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot be
calculated.
There are two evidences that indicate that agent could have
formed an opinion with til actually using ti :
13 / 19

57. Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot be
calculated.
There are two evidences that indicate that agent could have
formed an opinion with til actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels
(til ≥ ti ) would have led to opinion formation as well.
13 / 19

58. Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot be
calculated.
There are two evidences that indicate that agent could have
formed an opinion with til actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels
(til ≥ ti ) would have led to opinion formation as well.
2 Ev2: Otherwise, if til requires less updates to form an opinion
then the observed strongest support.
13 / 19

59. Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot be
calculated.
There are two evidences that indicate that agent could have
formed an opinion with til actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels
(til ≥ ti ) would have led to opinion formation as well.
2 Ev2: Otherwise, if til requires less updates to form an opinion
then the observed strongest support.
ˆ
hi (til ) ≈ hi (til )
13 / 19

60. Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
ˆ
The problem of selecting til ∈ Ti , accordingly their h(til ), resembles
the standard multi-armed bandit (MAB) model.
14 / 19

61. Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
ˆ
The problem of selecting til ∈ Ti , accordingly their h(til ), resembles
the standard multi-armed bandit (MAB) model.
The agent can apply MAB
strategies:
Greedy
-greedy
-N-greedy
Soft-max
– assume that reward
distribution is unknown.
14 / 19

62. Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
ˆ
The problem of selecting til ∈ Ti , accordingly their h(til ), resembles
the standard multi-armed bandit (MAB) model.
The agent can apply MAB However, for ascendantly ordered Ti :
1
Awareness Rate
strategies: 0.8
Greedy 0.6
0.4
-greedy 0.2
-N-greedy 0
0.55 0.6 tcritical 0.65 0.7
Soft-max Trust Level
– assume that reward Hill-climbing: Select a trust level from
distribution is unknown. the closest to the currently used
14 / 19

63. Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
ˆ
The problem of selecting til ∈ Ti , accordingly their h(til ), resembles
the standard multi-armed bandit (MAB) model.
The agent can apply MAB However, for ascendantly ordered Ti :
1
Awareness Rate
strategies: 0.8
Greedy 0.6
0.4
-greedy 0.2
-N-greedy 0
0.55 0.6 tcritical 0.65 0.7
Soft-max Trust Level
– assume that reward Hill-climbing: Select a trust level from
distribution is unknown. the closest to the currently used
Since an agent’s choice influences others, strategies with less
dramatic changes to the dynamics are expected to perform better.
14 / 19

64. Introduction Model AAT Experiments and Results Conclusions
Selection of the Target Awareness Rate
1
0.9
Reliability
0.8
0.7
0.6
0.8 0.85 0.9 0.95 1
Target awareness rate, hbest
random scalefree smallworld
The agents have to compromise their awareness rates to improve
team’s reliability.
15 / 19

65. Introduction Model AAT Experiments and Results Conclusions
Selection of the Target Awareness Rate
1
0.75
Average trust level, 〈ti 〉
0.9
0.7
Reliability
0.8
0.65
0.7
0.6 0.6
0.8 0.85 0.9 0.95 1 0.8 0.85 0.9 0.95 1
Target awareness rate, hbest Target awareness rate, hbest
random scalefree smallworld
The agents have to compromise their awareness rates to improve
team’s reliability.
With a high target awareness rate, hbest , a team exhibits unstable
dynamics, thus the reliability drops. 15 / 19

66. Introduction Model AAT Experiments and Results Conclusions
Reliability of a Team
(a) Random Network
1
0.9
AAT
0.8
Reliability
DACOR
Pre-tuned Trust Levels
0.7 Average Pre-tuned
Trust Levels
0.6
0.5
500 1000 1500 2000
Network Size
AAT significantly outperforms prediction of the best parameters
(average pre-tuned) and existing DACOR. Individually pre-tuned
trust levels indicate on the upper-bound that can be achieved.
16 / 19

67. Introduction Model AAT Experiments and Results Conclusions
Reliability of a Team
(b) Scale−Free Network
1
0.9
AAT
0.8
Reliability
DACOR
Pre-tuned Trust Levels
0.7 Average Pre-tuned
Trust Levels
0.6
0.5
500 1000 1500 2000
Network Size
AAT significantly outperforms prediction of the best parameters
(average pre-tuned) and existing DACOR. Individually pre-tuned
trust levels indicate on the upper-bound that can be achieved.
16 / 19

68. Introduction Model AAT Experiments and Results Conclusions
Reliability of a Team
(c) Small−World Network
1
0.9
AAT
0.8
Reliability
DACOR
Pre-tuned Trust Levels
0.7 Average Pre-tuned
Trust Levels
0.6
0.5
500 1000 1500 2000
Network Size
AAT significantly outperforms prediction of the best parameters
(average pre-tuned) and existing DACOR. Individually pre-tuned
trust levels indicate on the upper-bound that can be achieved.
16 / 19

69. Introduction Model AAT Experiments and Results Conclusions
Communication Expense
MinimalCommunication = NumberOfNeighbours
Agents
17 / 19

70. Introduction Model AAT Experiments and Results Conclusions
Communication Expense
MinimalCommunication = NumberOfNeighbours
Agents
80
Messages per Agent
60
AAT
40 DACOR
Minimal
Communication
20
0
500 1000 1500 2000
Network Size
AAT is communicationally efficient while DACOR requires 4-7
times more messages to operate
17 / 19

71. Introduction Model AAT Experiments and Results Conclusions
Performance in the Presence of Indifferent Agents
(a) Random Network
1
0.9
Reliability
0.8 AAT
DACOR
Pre-tuned Trust Levels
0.7 Average Pre-tuned
Trust Levels
0.6
0.5
0 20 40 60 80 100
% of Indifferent Agents
AAT installed on a half of a team delivers higher reliability than we 18 / 19

72. Introduction Model AAT Experiments and Results Conclusions
Performance in the Presence of Indifferent Agents
(b) Scale−Free Network
1
0.9
0.8
Reliability
AAT
0.7 DACOR
Pre-tuned Trust Levels
Average Pre-tuned
0.6 Trust Levels
0.5
0.4
0 20 40 60 80 100
% of Indifferent Agents
AAT installed on a half of a team delivers higher reliability than we
can predict by using the average pre-tuned trust-levels.
18 / 19

73. Introduction Model AAT Experiments and Results Conclusions
Performance in the Presence of Indifferent Agents
(c) Small−World Network
1
0.9
0.8
Reliability
AAT
0.7 DACOR
Pre-tuned Trust Levels
Average Pre-tuned
0.6 Trust Levels
0.5
0.4
0 20 40 60 80 100
% of Indifferent Agents
AAT installed on a half of a team delivers higher reliability than we
can predict by using the average pre-tuned trust-levels.
18 / 19

74. Introduction Model AAT Experiments and Results Conclusions
Conclusions
AAT exploits properties of social behaviour to improve accuracy of
agents’ opinions. Contributions:
improves Reliability
minimises Communication – the first to operate under this
restriction
Computationally inexpensive
Adaptive, Scalable, Robust to the presence of indifferent
agents
19 / 19

75. Introduction Model AAT Experiments and Results Conclusions
Conclusions
AAT exploits properties of social behaviour to improve accuracy of
agents’ opinions. Contributions:
improves Reliability
minimises Communication – the first to operate under this
restriction
Computationally inexpensive
Adaptive, Scalable, Robust to the presence of indifferent
agents
Future work:
Tuning an individual trust level for each neighbour
Attack-resistant solution
19 / 19