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quality is presented. The focus in this work is on the possible criteria that is required to
build a trust model. However, the uncertainty in an agent's behavior and how it can be
captured is not considered.
The work of [12] estimates the trust of an agent considering “direct experience”,
“witness information”, “role-based rules” and “third-party references provided by the
target agents”. Although the latter 2 aspects are not included in our model, it is based on
the assumption that the agents are honest in exchanging information with one another.
In addition, despite the fact that the underlying trust of an agent is assumed to have
a normal distribution, the estimated trust is a single value instead of a distribution. In
other words, it does not try to measure the uncertainty associated with the occurrence
of each outcome of the domain considering the results of the empirical experiments.
In all of the above works the uncertainty in the trust of an agent is not considered.
We now review the works that address uncertainty. Reece et al. [3] present a multi-
dimensional trust in which each dimension is binary (successful or unsuccessful) and
corresponds to a service provided in a contract (video, audio, data service, etc.). This
paper is mainly concentrated on fusing information received from agents who had direct
observations over a subset of services (incomplete information) to derive the complete
information on the target entity while our work focuses on having an accurate estimation
when there is manipulation in the acquired information.
Yu and Singh [13] measure the probability of trust, distrust and uncertainty of an
agent based on the outcomes of inter-agent interactions. The uncertainty measured in
this work is equal to the frequency of the interaction results in which the agent's perfor-
mance is neither highly trustworthy nor highly untrustworthy which can be inferred as
lack of both trust and distrust in the agent. However, the uncertainty that we capture is
due to lack of adequate information on an agent's trust or the variability in the agent's
degree of trustworthiness regardless of how trustworthy the agent is. In other words,
when an agent acts with high uncertainty it's degree of trustworthiness is hard to pre-
dict for future interactions. We do not consider uncertainty as lack of trust or distrust,
but the variability in the degree of trustworthiness. In both works of [13] and [3] the
possibility of having malicious agents providing falsified reports is ignored. The works
of [1] and [2] provide probabilistic computational models measuring belief, disbelief
and uncertainty from binary interactions (positive or negative). Although the manipu-
lation of information by the reporter agents is not considered in these works, they split
the interval of [0 , 1] between these 3 elements measuring a single value for each one of
them. We do not capture uncertainty in the same sense by measuring a single value, in-
stead we consider uncertainty by measuring the likelihood of occurrence of every trust
element in the domain and therefore capturing the possible deviation in the degree of
trustworthiness of the agents.
One of the closest works to our model which includes both uncertainty and the ma-
nipulation of information is Travos [4]. Although this work has a strong probabilistic
approach and covers many issues, it is yet restricted to binary domain of events where
each interaction, which is driven from the underlying probability that an agent fulfills
it's obligations, is either successful or unsuccessful. Our work is a generalization of this
work in the sense that it is extended to a multi-valued domain where we associate a
probability to the occurrence of each trust value in the domain. Extension of the Travos
 
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