Efficient Sharing of Conflicting Opinions with Minimal Communication in Large Decentralised Teams
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Introduction Model AAT Experiments and Results Conclusions
Efficient Sharing of Conflicting Opinions withMinimal 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
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).
2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?
1 / 19
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?
1 / 19
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?
1 / 19
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?
1 / 19
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?
1 / 19
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?
1 / 19
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?
1 / 19
Introduction Model AAT Experiments and Results Conclusions
Disaster response and large decentralised teams
2010, Haiti earthquake – Citizen and publicnews reporting, plotted on an online map(Ushahidi).2010, Chile earthquake – Twitter is one ofthe speediest, albeit not the most accurate,sources of real-time information (France24).
Large teams of individuals
Decentralised
Not every individual can make anobservation
Observations are uncertain and conflicting
Individuals share opinions withoutsupporting information
How opinions are shared and how to improve their accuracy?1 / 19
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 ofConceptcion
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
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 ofConceptcion
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
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 ofConceptcion
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
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 ofConceptcion
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
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 ofConceptcion
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
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 ofConceptcion
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
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 ofConceptcion
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
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, observationsbased on information from others
by analysing communicated informationreaching 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, andfilter out incorrect opinions in the sharing process
How to find such settings by independent actions of the agents?
3 / 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, observationsbased on information from others
by analysing communicated informationreaching 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, andfilter out incorrect opinions in the sharing process
How to find such settings by independent actions of the agents?
3 / 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, observationsbased on information from others
by analysing communicated informationreaching 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, andfilter out incorrect opinions in the sharing process
How to find such settings by independent actions of the agents?
3 / 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, observationsbased on information from others
by analysing communicated informationreaching 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, andfilter out incorrect opinions in the sharing process
How to find such settings by independent actions of the agents?
3 / 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, observationsbased on information from others
by analysing communicated informationreaching 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, andfilter out incorrect opinions in the sharing process
How to find such settings by independent actions of the agents?3 / 19
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
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Agent
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
Subject ofinterest
Agent
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
Belief1Prior0
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
Belief
Own observations
1Prior0
Updated with:sensors
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
Belief
Own observations
1Prior0
Updated with:sensors
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
Belief
Own observations
Opinions' of othersYes No? No...
1Prior0
Updated with:sensors
network neighbours
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
Belief
Own observations
Opinions' of othersYes No? No...
1Prior0
Updated with:sensors
network neighbours
5 / 19
Introduction Model AAT Experiments and Results Conclusions
Model – an Agent
Will it rain tonight?
YesNo Don't know
Subject ofinterest
Opinion
Agent
Belief
Own observations
Opinions' of othersYes No? No...
1Prior0
Updated with:sensors
network neighbours
5 / 19
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 thecorresponding opinions. (b = white)
opinions are shared incascades
cascades might bewrong and fragile
cascades depend ontrust levels
double counting fallacy
6 / 19
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 thecorresponding opinions. (b = white)
opinions are shared incascades
cascades might bewrong and fragile
cascades depend ontrust levels
double counting fallacy
6 / 19
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 thecorresponding opinions. (b = white)
opinions are shared incascades
cascades might bewrong and fragile
cascades depend ontrust levels
double counting fallacy
6 / 19
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 thecorresponding opinions. (b = white)
opinions are shared incascades
cascades might bewrong and fragile
cascades depend ontrust levels
double counting fallacy
6 / 19
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 thecorresponding opinions. (b = white)
opinions are shared incascades
cascades might bewrong and fragile
cascades depend ontrust levels
double counting fallacy
6 / 19
Introduction Model AAT Experiments and Results Conclusions
Settings for Improved Reliability – Metrics
0.55 0.6 0.65 0.7 0.750
20
40
60
80
100Agentsholdingopinion,%
tcritical
correctincorrectundetermined
0.55 0.6 0.65 0.7 0.750
0.2
0.4
0.6
0.8
1
Reliability
Trust level (common for all agents)
tcritical
Reliability
Awareness
Scale-Invariant dynamicsStable dynamics Unstable dynamics
7 / 19
Introduction Model AAT Experiments and Results Conclusions
Cascades Distribution
100 101 102 103100
101
102
103
104t=0.6
100 101 102 103100
101
102
103
104t=0.63
100 101 102 103100
101
102t=0.66
Cas
cade
Fre
quen
cy
Size of Opinion Cascade
Cas
cade
Fre
quen
cy
Size of Opinion Cascade
Cas
cade
Fre
quen
cy
Size of Opinion Cascade
Scale-Invariant DynamicsStable Dynamics Unstable Dynamics
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
Introduction Model AAT Experiments and Results Conclusions
Cascades Distribution
100 101 102 103100
101
102
103
104t=0.6
100 101 102 103100
101
102
103
104t=0.63
100 101 102 103100
101
102t=0.66
Cas
cade
Fre
quen
cy
Size of Opinion Cascade
Cas
cade
Fre
quen
cy
Size of Opinion Cascade
Cas
cade
Fre
quen
cy
Size of Opinion Cascade
Scale-Invariant DynamicsStable Dynamics Unstable Dynamics
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
Introduction Model AAT Experiments and Results Conclusions
DACORYes
Yes
?
?
?
?
α
introduces additional communication〈NumberOfNeighbours〉2 additional messages for a singleopinion change
exhibits low adaptivityrequires tuning of its parameters
9 / 19
Introduction Model AAT Experiments and Results Conclusions
DACORYes
Yes
?
?
?
?
α
introduces additional communication〈NumberOfNeighbours〉2 additional messages for a singleopinion change
exhibits low adaptivityrequires tuning of its parameters
9 / 19
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on localobservations only?
Intuition
An agent must use the minimal trust level that still enables it toform its opinion
However, the agent’s choice influences others in the team
10 / 19
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on localobservations only?
0.55 0.6 0.65 0.7 0.750
0.2
0.4
0.6
0.8
1
Rel
iabi
lity
Trust level (common for all agents)
tcritical
Reliability
Awareness
Scale-Invariant dynamicsStable dynamics Unstable dynamics
Intuition
An agent must use the minimal trust level that still enables it toform its opinion
However, the agent’s choice influences others in the team
10 / 19
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on localobservations only?
0.55 0.6 0.65 0.7 0.750
0.2
0.4
0.6
0.8
1
Rel
iabi
lity
Trust level (common for all agents)
tcritical
Reliability
Awareness
Scale-Invariant dynamicsStable dynamics Unstable dynamics
Intuition
An agent must use the minimal trust level that still enables it toform its opinion
However, the agent’s choice influences others in the team
10 / 19
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
How to find the settings for improved reliability based on localobservations only?
0.55 0.6 0.65 0.7 0.750
0.2
0.4
0.6
0.8
1
Rel
iabi
lity
Trust level (common for all agents)
tcritical
Reliability
Awareness
Scale-Invariant dynamicsStable dynamics Unstable dynamics
Intuition
An agent must use the minimal trust level that still enables it toform its opinion
However, the agent’s choice influences others in the team10 / 19
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level t li from the candidates.
The agent with t li has to achieve the target awareness rate, hbest
ti = arg mint li
|hi (t li )− hbest|
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
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level t li from the candidates.The agent with t li has to achieve the target awareness rate, hbest
ti = arg mint li
|hi (t li )− hbest|
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
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level t li from the candidates.The agent with t li has to achieve the target awareness rate, hbest
ti = arg mint li
|hi (t li )− hbest|
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
Introduction Model AAT Experiments and Results Conclusions
Autonomous Adaptive Tuning of Trust Levels
Agent i has to select minimal trust level t li from the candidates.The agent with t li has to achieve the target awareness rate, hbest
ti = arg mint li
|hi (t li )− hbest|
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
Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
1P'i0 σ1-σ
Pki
oi =bl
ack
k=3
o i =white
k=12
To form the most accurate opinion the agent must form its opinionwhen it observes the strongest support.
Since the number of neighbours |Ni | is limited, the set of thecandidate trust levels is:
Ti = {t l−i , t l+i : l = 1 . . . |Ni |}
In the settings of dynamic topology and agent may use arbitrary Ti
12 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
1P'i0 σ1-σ 0.5
Pki
oi =bl
ack
k=1
ti1− ti
1+o i
=white
k=1
1P'i0 σ1-σ 0.5
Pki
ti2− ti
2+
To form the most accurate opinion the agent must form its opinionwhen it observes the strongest support.
Since the number of neighbours |Ni | is limited, the set of thecandidate trust levels is:
Ti = {t l−i , t l+i : l = 1 . . . |Ni |}
In the settings of dynamic topology and agent may use arbitrary Ti
12 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
1P'i0 σ1-σ 0.5
Pki
oi =bl
ack
k=1
ti1− ti
1+o i
=white
k=1
1P'i0 σ1-σ 0.5
Pki
ti2− ti
2+
To form the most accurate opinion the agent must form its opinionwhen it observes the strongest support.
Since the number of neighbours |Ni | is limited, the set of thecandidate trust levels is:
Ti = {t l−i , t l+i : l = 1 . . . |Ni |}
In the settings of dynamic topology and agent may use arbitrary Ti
12 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Candidate Trust Levels
1P'i0 σ1-σ 0.5
Pki
oi =bl
ack
k=1
ti1− ti
1+o i
=white
k=1
1P'i0 σ1-σ 0.5
Pki
ti2− ti
2+
To form the most accurate opinion the agent must form its opinionwhen it observes the strongest support.
Since the number of neighbours |Ni | is limited, the set of thecandidate trust levels is:
Ti = {t l−i , t l+i : l = 1 . . . |Ni |}
In the settings of dynamic topology and agent may use arbitrary Ti
12 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot becalculated.
There are two evidences that indicate that agent could haveformed an opinion with t li actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels(t li ≥ ti ) would have led to opinion formation as well.
2 Ev2: Otherwise, if t li requires less updates to form an opinionthen the observed strongest support.
hi (tli ) ≈ hi (t
li )
13 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot becalculated.There are two evidences that indicate that agent could haveformed an opinion with t li actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels(t li ≥ ti ) would have led to opinion formation as well.
2 Ev2: Otherwise, if t li requires less updates to form an opinionthen the observed strongest support.
hi (tli ) ≈ hi (t
li )
13 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot becalculated.There are two evidences that indicate that agent could haveformed an opinion with t li actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels(t li ≥ ti ) would have led to opinion formation as well.
2 Ev2: Otherwise, if t li requires less updates to form an opinionthen the observed strongest support.
hi (tli ) ≈ hi (t
li )
13 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot becalculated.There are two evidences that indicate that agent could haveformed an opinion with t li actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels(t li ≥ ti ) would have led to opinion formation as well.
2 Ev2: Otherwise, if t li requires less updates to form an opinionthen the observed strongest support.
hi (tli ) ≈ hi (t
li )
13 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Estimation of the Awareness Rates
The awareness rates of the candidate trust levels cannot becalculated.There are two evidences that indicate that agent could haveformed an opinion with t li actually using ti :
1 Ev1: If an opinion was formed, then all higher trust levels(t li ≥ ti ) would have led to opinion formation as well.
2 Ev2: Otherwise, if t li requires less updates to form an opinionthen the observed strongest support.
hi (tli ) ≈ hi (t
li )
13 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
The problem of selecting t li ∈ Ti , accordingly their h(t li ), resemblesthe standard multi-armed bandit (MAB) model.
The agent can apply MABstrategies:
Greedy
ε-greedy
ε-N-greedy
Soft-max
– assume that rewarddistribution is unknown.
However, for ascendantly ordered Ti :
0.55 0.6 0.65 0.70
0.2
0.4
0.6
0.8
1
Aw
aren
ess
Rat
e
Trust Leveltcritical
Hill-climbing: Select a trust level fromthe closest to the currently used
Since an agent’s choice influences others, strategies with lessdramatic changes to the dynamics are expected to perform better.
14 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
The problem of selecting t li ∈ Ti , accordingly their h(t li ), resemblesthe standard multi-armed bandit (MAB) model.
The agent can apply MABstrategies:
Greedy
ε-greedy
ε-N-greedy
Soft-max
– assume that rewarddistribution is unknown.
However, for ascendantly ordered Ti :
0.55 0.6 0.65 0.70
0.2
0.4
0.6
0.8
1
Aw
aren
ess
Rat
e
Trust Leveltcritical
Hill-climbing: Select a trust level fromthe closest to the currently used
Since an agent’s choice influences others, strategies with lessdramatic changes to the dynamics are expected to perform better.
14 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
The problem of selecting t li ∈ Ti , accordingly their h(t li ), resemblesthe standard multi-armed bandit (MAB) model.
The agent can apply MABstrategies:
Greedy
ε-greedy
ε-N-greedy
Soft-max
– assume that rewarddistribution is unknown.
However, for ascendantly ordered Ti :
0.55 0.6 0.65 0.70
0.2
0.4
0.6
0.8
1
Aw
aren
ess
Rat
eTrust Level
tcritical
Hill-climbing: Select a trust level fromthe closest to the currently used
Since an agent’s choice influences others, strategies with lessdramatic changes to the dynamics are expected to perform better.
14 / 19
Introduction Model AAT Experiments and Results Conclusions
AAT – Strategies to Select a Trust Level
The problem of selecting t li ∈ Ti , accordingly their h(t li ), resemblesthe standard multi-armed bandit (MAB) model.
The agent can apply MABstrategies:
Greedy
ε-greedy
ε-N-greedy
Soft-max
– assume that rewarddistribution is unknown.
However, for ascendantly ordered Ti :
0.55 0.6 0.65 0.70
0.2
0.4
0.6
0.8
1
Aw
aren
ess
Rat
eTrust Level
tcritical
Hill-climbing: Select a trust level fromthe closest to the currently used
Since an agent’s choice influences others, strategies with lessdramatic changes to the dynamics are expected to perform better.
14 / 19
Introduction Model AAT Experiments and Results Conclusions
Selection of the Target Awareness Rate
0.8 0.85 0.9 0.95 10.6
0.7
0.8
0.9
1
Target awareness rate, hbest
Reliability
0.8 0.85 0.9 0.95 10.6
0.65
0.7
0.75
Target awareness rate, hbest
Averagetrustlevel,⟨ti⟩
random scalefree smallworld
The agents have to compromise their awareness rates to improveteam’s reliability.
With a high target awareness rate, hbest, a team exhibits unstabledynamics, thus the reliability drops.
15 / 19
Introduction Model AAT Experiments and Results Conclusions
Selection of the Target Awareness Rate
0.8 0.85 0.9 0.95 10.6
0.7
0.8
0.9
1
Target awareness rate, hbest
Reliability
0.8 0.85 0.9 0.95 10.6
0.65
0.7
0.75
Target awareness rate, hbest
Averagetrustlevel,⟨ti⟩
random scalefree smallworld
The agents have to compromise their awareness rates to improveteam’s reliability.With a high target awareness rate, hbest, a team exhibits unstabledynamics, thus the reliability drops. 15 / 19
Introduction Model AAT Experiments and Results Conclusions
Reliability of a Team
500 1000 1500 20000.5
0.6
0.7
0.8
0.9
1(a) Random Network
Rel
iabi
lity
Network Size
AATDACORPre-tuned Trust LevelsAverage Pre-tunedTrust Levels
AAT significantly outperforms prediction of the best parameters(average pre-tuned) and existing DACOR. Individually pre-tunedtrust levels indicate on the upper-bound that can be achieved.
16 / 19
Introduction Model AAT Experiments and Results Conclusions
Reliability of a Team
500 1000 1500 20000.5
0.6
0.7
0.8
0.9
1(b) Scale−Free Network
Rel
iabi
lity
Network Size
AATDACORPre-tuned Trust LevelsAverage Pre-tunedTrust Levels
AAT significantly outperforms prediction of the best parameters(average pre-tuned) and existing DACOR. Individually pre-tunedtrust levels indicate on the upper-bound that can be achieved.
16 / 19
Introduction Model AAT Experiments and Results Conclusions
Reliability of a Team
500 1000 1500 20000.5
0.6
0.7
0.8
0.9
1(c) Small−World Network
Rel
iabi
lity
Network Size
AATDACORPre-tuned Trust LevelsAverage Pre-tunedTrust Levels
AAT significantly outperforms prediction of the best parameters(average pre-tuned) and existing DACOR. Individually pre-tunedtrust levels indicate on the upper-bound that can be achieved.
16 / 19
Introduction Model AAT Experiments and Results Conclusions
Communication Expense
MinimalCommunication =∑
Agents
NumberOfNeighbours
AAT is communicationally efficient while DACOR requires 4-7times more messages to operate
17 / 19
Introduction Model AAT Experiments and Results Conclusions
Communication Expense
MinimalCommunication =∑
Agents
NumberOfNeighbours
500 1000 1500 20000
20
40
60
80
Mes
sage
s pe
r Age
nt
Network Size
DACOR
Minimal Communication
AAT
AAT is communicationally efficient while DACOR requires 4-7times more messages to operate
17 / 19
Introduction Model AAT Experiments and Results Conclusions
Performance in the Presence of Indifferent Agents
0 20 40 60 80 1000.5
0.6
0.7
0.8
0.9
1(a) Random Network
Rel
iabi
lity
% of Indifferent Agents
AATDACORPre-tuned Trust LevelsAverage Pre-tunedTrust Levels
AAT installed on a half of a team delivers higher reliability than wecan predict by using the average pre-tuned trust-levels.
18 / 19
Introduction Model AAT Experiments and Results Conclusions
Performance in the Presence of Indifferent Agents
0 20 40 60 80 1000.4
0.5
0.6
0.7
0.8
0.9
1(b) Scale−Free Network
Rel
iabi
lity
% of Indifferent Agents
AATDACORPre-tuned Trust LevelsAverage Pre-tunedTrust Levels
AAT installed on a half of a team delivers higher reliability than wecan predict by using the average pre-tuned trust-levels.
18 / 19
Introduction Model AAT Experiments and Results Conclusions
Performance in the Presence of Indifferent Agents
0 20 40 60 80 1000.4
0.5
0.6
0.7
0.8
0.9
1(c) Small−World Network
Rel
iabi
lity
% of Indifferent Agents
AATDACORPre-tuned Trust LevelsAverage Pre-tunedTrust Levels
AAT installed on a half of a team delivers higher reliability than wecan predict by using the average pre-tuned trust-levels.
18 / 19
Introduction Model AAT Experiments and Results Conclusions
Conclusions
AAT exploits properties of social behaviour to improve accuracy ofagents’ opinions. Contributions:
improves Reliability
minimises Communication – the first to operate under thisrestriction
Computationally inexpensive
Adaptive, Scalable, Robust to the presence of indifferentagents
Future work:
Tuning an individual trust level for each neighbour
Attack-resistant solution
19 / 19
Introduction Model AAT Experiments and Results Conclusions
Conclusions
AAT exploits properties of social behaviour to improve accuracy ofagents’ opinions. Contributions:
improves Reliability
minimises Communication – the first to operate under thisrestriction
Computationally inexpensive
Adaptive, Scalable, Robust to the presence of indifferentagents
Future work:
Tuning an individual trust level for each neighbour
Attack-resistant solution
19 / 19
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