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See All Customer Reviews. Shop Books. Read an excerpt of this book! Add to Wishlist. USD Sign in to Purchase Instantly. Explore Now. In fact in Regret the agents can evaluate trust by themselves, without any reference to a central mechanism. Similarly to SPORAS, Regret measures the predictive power of each trust value through the two reliability measures: the number of ratings taken into account in producing the value of trust and the deviation of these ratings.
In Regret the agents can also share their opinions. To this end the system develops a very interesting and sophisticated witness reputation component for aggregating witness reports. Finally, Regret introduces the concepts of neighbourhood reputation and system reputation. With system reputation is meant a mechanism to assign default trust values to the target agent based on its social role in an interaction e.
Concluding Regret uses various sources of trust information, is decentralized, and as a consequence, satisfies the requirements for modelling trust in multi-agent systems.
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Its main limit is about the fact that it does not specify how the social networks are to be built. It is a decentralized model designed for general applications based on a variety of trust sources.
In particular they show that: r Agents using the trust measure provided by FIRE are able to select reliable partners for interactions and, thus, obtain better utility gain compared to those using no trust measure. The main problem and limit with FIRE is about its assumption that agents are honest in exchanging information with one another: The authors are aware of this limit and are working to introduce reliability measures for witness ratings and certified ratings. Different Kinds of Metrics The computational approaches to trust can be classified also on the basis of the different forms of metrics to rate the trust performances.
An analysis about the metrics of the differ- ent approaches is particularly relevant because it should give account about the underlying assumptions of these approaches. But there are also discrete verbal statements like those used in Abdul-Rahman and Hailes, : Very Trustworthy, Trustwor- thy, Untrustworthy, Very Untrustworthy, that give a more direct representation of the human evaluations: The problem is then to translate these statements in adequate misures for the computational process.
We can cite Schillo et al. Probabilistic approaches have the advantage of exploiting the consolidated apparatus of the probabilistic methods: in particular they can profit from the different derivation methods from the probability calculus to the advanced statistical methods Josang, , Krukow and Nielsen, An interesting and useful approach is given by the Belief Theory that responds to the limits of the probabilistic approaches with respect to uncertain information. In fact, the aim of belief theory is to give a formal representation of the inaccurate and uncertain aspect of information.
In this case the sum of the probabilities over all possible outcomes do not necessarily sum up to 1 the remaining probability can be interpreted as uncertainty. Trust and Technology We have to say that each of these metrics can present both advantages and problems, but what is important in our view is the fact that trust has an intrinsic multi-factorial nature and this peculiar feature has to be represented and implemented also in the quantitative aspects of this phenomenon see Chapter 3.
One of the more interesting attempts in this direction is represented by the REGRET approach where to overcome the mono-dimensionality of the trust performance some fuzziness over the notion of performance itself is used. REGRET introduces a rich semantics for the ratings called impressions by defining their specific features for example: delivery date, price, and so on. On the basis of the fuzzy reasoning techniques, the system is able to compose the different dimensions producing a general and global impression of one agent on another.
With respect to the fuzzy approach applied to the trust evaluation we showed in Chapter 11 a specific implementation based on the socio-cognitive approach to trust. Other Models and Approaches to Trust in the Computational Framework Recent works on designing models for propagating trust in social networks and for selecting the most appropriate services in the service-oriented environments are of particular interest to analyze. In Yu and Singh, the models of trust propagation are distinguished by: r Trust in expertise: ability of providing services; and r Trust in sociability: ability of providing referrals.
With regard to the latter, if we use trust to evaluate people and information we have to compute trust between people who do not know one another and expect to achieve the result that each agent in the network will evaluate the trustworthiness of its potential, often anonymous, partners Golbeck, In CertProp model Hang et al.
In this approach a social network system of interacting agents is modeled as a directed graph with weights on the links: a sort of social graph, in which the nodes represent the users and the edges represent the relationships between these users the social graph representation is quite widespread and used, see also Ziegler, , Levien, In any case this work, defining a set of algebric properties for the three operators, determines trust propagation in an efficient and accurate way even if in a simplified domain. Another interesting question analyzed in this work is the classical mapping between opinions and evidences: in the networks the weights are subjective opinions and not objective evidence.
This transformation also allows the idea that the average opinion yields the lower certainty of transformed trust. It helps to reduce the subjectivity in opinion-based datasets so that the evidence-based approaches like Cert-Prop can apply. The attention to the mathematical properties of the operators, fails sometimes to catch the deeper nature of the trust phenomenon. Not necessarily is the final value of trust a mix of the various values. Or I might have an optimistic attitude and adopt always the best, more favorable estimation.
In Richardson et al. This method first enumerates all paths between the user and every other user who has a local belief in a given statement. Then, the belief associated with each path concatenation operator is calculated, and combined with the beliefs associated with all paths aggregation operator.
Trust metrics1 compute quantitative estimates of how much trust an agent X should have in Y, taking into account trust ratings from other agents on the network. Two main important applications of trust metrics are: Advogate Levien, and Apple- seed Ziegler, Both these metrics can be classified as local group trust metrics. Local is intended versus Global: where Global take into account all peers in the network and the links connecting them; while Local trust metrics take into account personal bias. They operate on partial trust graph information the web of trust for an agent X is the set of relationships emanating from X and passing through nodes X directly or indirectly trusts.
Advogate computes a set of accepted nodes in three steps. First, it is assigned a capacity to every node as a function of the shortest path distance from the seed to that node. Second, there is a transformation of the graph, adding extra edges from each node to a special node called supersink. Third, it is computs the maximum network flow for the new graph: the accepted nodes are those that have a flow across the special node supersink.
In contrast to Advogate, Appleseed uses spreading activation Quilian, It spreads energy across the graph, and when propagated through a node, divides energy among sucessors based on the edge weights. The main idea in Appleseed is to simulate the spectral decomposition and it requires several iterations to converge towards the set of acceptable nodes.
Trust and Technology Another interesting work about trust propagation in social networks is focused on a different aspect of trust inference: in particular, the change in trust values in the network and the impact of that change on the results of the existing algorithms.
In their work Golbeck and Kuter, , Golbeck and Kuter show an experimental study to understand the behavior of different trust inference algorithms with respect to the changes occuring in the social network. How large is the impact of that change? How does this relate to the type of inference algorithm? They show the relevance of the chosen algorithm in all three questions. As we have claimed above the problem of selecting trustworthy services on the web is becoming really relevant given the variety and quantity of the offer.
Toward a testbed for evaluating computational trust models: experiments and analysis
In particular, two main problems should be taken in consideration: a The trust evaluation of a service should take into consideration the fact that very often a service is a composed service with different providers, functions and responsibilities. In fact, while works exist about modeling trustworthiness of individual service providers, very few results were reached in modeling groups of providers working to a specific composed service.
Hafizoglu and Yolum Hafizoglu and Yolum, , propose a group trust model to under- stand the behavior of such teams that realize a composed service. Their work is in turn based on the service graphs model Yolum and Singh, where graphs are helpful for reasoning about services that are related to each other.
So the individual features of the agents in providing specific services are not so useful for selecting the right best team of the composed service. Another interesting work on this problem is Hang and Singh, in which is proposed a trust-aware service selection model based on a Bayessan network. The model evaluates the service trustworthiness on both direct and indirect from referrals experience.
The method models causal relationships between services with Bayesan networks. The main characteristic of this model is that it can deal with incomplete observation in such a way taking into account the possibility that underlying services may not be exposed to the consumer for this introduces a specific parameter representing the percentage of missing data. This is true with mechanical engineering factories, cars, tractors, etc, but is much more important with cognitive and social technologies: like computers, web, and their mediation and support of the entire human individual and social activity, from study and learning to work, from friendship and communities to political participation, from market and business to smart learning environments.
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We have to design hand in hand with technology the cognitive, interactive, and collective dimensions. More precisely, we have to design technology with those incorporated dimensions. But in order to do this one should have the appropriate understanding of those dimensions and some theoretical abstraction of them and some possible modeling of them. Otherwise we proceed just in an empirical, haphazard trials and errors way.
This is why we believe that a deep and complete model of trust including the cognitive, emotional, decisional, social, institutional dimension be not just useful but necessary. A technology endowed with autonomous learning, decentralization, acquisition of local and timely information; able to reason and solve problems; endowed with some proactivity and a real collaborative not just executive attitude.
But if this is true this will make even more central the role of trust and delegation and of their modelling. References Abdul-Rahman, A. Abrams, M. Computers and Security, 14 1 : 45— Anderson, R.
Antoniou, G. The MIT Press. Baecker, R.
itlauto.com/wp-includes/parental/368-urgent-recherche-application.php A multidisciplinary approach. Barber, S. Bishop, M. Reading, MA: Addison-Wesley. Blaze, M.
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Card, S. Castelfranchi, C. Trust and Technology Castelfranchi, C.