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(a) Internal Prob. Dist. of
a
(b) Network of Agents
Fig. 1.
Multi-Agent Platform
The second manipulation algorithm is the following:
Algorithm II
for
each
τ
∈
T
do
τ
←
random trust rating from
T
, according
to agent
a
's internal trust distribution
max
error
τ
=1
τ
error
τ
= random value in
[0
,
max
error
τ
]
Π
a→a
D
(
τ
)=
Π
a→a
D
(
τ
)+
error
τ
end for
−
As for algorithm I, the distribution of
Π
a→a
D
(
τ
)
,∀τ ∈ T
is normalized before being
reported to
a
S
. In algorithm II, an additional random selection value is added where the
random value is selected uniformly in
[0
,
max
error
τ
]
. In algorithm I, the trust rating
of
τ
PEA
a
and the trust values next to it have a high probability of being selected. The
error added to
Π
a→a
D
(
τ
)
may be neglected when the distribution is normalized. How-
ever, in algorithm II, if an agent is highly untrustworthy the random trust value of
τ
is
close to
τ
and thereupon the error value of
max
error
τ
is close to 1. This causes the
uniformly generated value in
[0
,
max
error
τ
]
considerably random and unpredictable
which makes the derived possibility distribution highly erroneous after normalization.
On the other hand, if an agent is highly trustworthy, the error value of
max
error
τ
is
close to zero and the random value generated in
[0
,
max
error
τ
]
would be even smaller,
making the error of the final possibility distribution insignificant. While incorporating
some random process, both algorithms manipulate the possibility distribution based on
the agent's degree of trustworthiness causing the scale of manipulation by more trust-
worthy agents smaller and vice-versa. However, the second algorithm acts more ran-
domly. We provide these algorithms to observe the extent of dependency of the derived
results with respect to a specific manipulation algorithm employed.
3.6
Game Scenario
In this paper, we study a model arising in social networks where agent
a
S
makes a num-
ber of interactions with each agent
a
in a set
A
=
{
a
1
,a
2
,...,a
n
}
of n agents (agent
a
S
's advisors), assuming each agent
a
A
has carried out some interactions with agent
a
D
. Agent
a
S
builds a possibility distribution of trust for each agent in
A
by usage of the
empirical data derived throughout their interactions, as described in Section 3.4. Each
∈
