TY - GEN
T1 - Subjective trust inference in composite services
AU - Li, Lei
AU - Wang, Yan
N1 - Copyright the Publisher. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2010
Y1 - 2010
N2 - In Service-Oriented Computing (SOC) environments, the trustworthiness of each service is critical for a ser vice client when selecting one from a large pool of ser vices. The trust value of a service is usually in the range of [0,1] and is evaluated from the ratings given by service clients, which represent the subjective belief of these service clients on the satisfaction of delivered services. So a trust value can be taken as the subjective probability, with which one party believes that another party can perform an action in a certain situation. Hence, subjective probability theory should be adopted in trust evaluation. In addition, in SOC environments, a service usually invokes other services offered by different service providers forming a composite service. Thus, the global trust of a composite service should be evaluated based on complex invocation structures. In this paper, firstly, based on Bayesian inference, we propose a novel method to evaluate the subjective trust worthiness of a service component from a series of ratings given by service clients. Secondly, we interpret the trust dependency caused by service invocations as conditional probability, which is evaluated based on the subjective trust values of service components. Further more, we propose a joint subjective probability method to evaluate the subjective global trust of a composite service on the basis of trust dependency. Finally, we in troduce the results of our conducted experiments to illustrate the properties of our proposed subjective global trust inference method.
AB - In Service-Oriented Computing (SOC) environments, the trustworthiness of each service is critical for a ser vice client when selecting one from a large pool of ser vices. The trust value of a service is usually in the range of [0,1] and is evaluated from the ratings given by service clients, which represent the subjective belief of these service clients on the satisfaction of delivered services. So a trust value can be taken as the subjective probability, with which one party believes that another party can perform an action in a certain situation. Hence, subjective probability theory should be adopted in trust evaluation. In addition, in SOC environments, a service usually invokes other services offered by different service providers forming a composite service. Thus, the global trust of a composite service should be evaluated based on complex invocation structures. In this paper, firstly, based on Bayesian inference, we propose a novel method to evaluate the subjective trust worthiness of a service component from a series of ratings given by service clients. Secondly, we interpret the trust dependency caused by service invocations as conditional probability, which is evaluated based on the subjective trust values of service components. Further more, we propose a joint subjective probability method to evaluate the subjective global trust of a composite service on the basis of trust dependency. Finally, we in troduce the results of our conducted experiments to illustrate the properties of our proposed subjective global trust inference method.
UR - http://www.scopus.com/inward/record.url?scp=77958603848&partnerID=8YFLogxK
U2 - 10.1609/aaai.v24i1.7504
DO - 10.1609/aaai.v24i1.7504
M3 - Conference proceeding contribution
AN - SCOPUS:77958603848
SN - 9781577354666
VL - 3
T3 - Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010
SP - 1377
EP - 1384
BT - AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
A2 - Fox, Maria
A2 - David, Poole
PB - Association for the Advancement of Artificial Intelligence
CY - Palo Alto, California
T2 - 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
Y2 - 11 July 2010 through 15 July 2010
ER -