A new trust model using Hidden Markov Model based mixture of experts

In service-oriented computing, selection of an appropriate web service is a challenging problem. The more services are available, the more difficult is the service selection. Trust and reputation mechanisms have been used to filter good services from bad ones. Trust and reputation system of web services can often be modeled as a multi-agent system where agents are used to manage and reason about trust and reputation on behalf of their users providing or consuming services. In this paper, we propose a trust establishment framework for such a system based on direct experience and recommended trust. While making trust based decision of accessing a web service from a service provider, the value of the trust on which the decision is based is predicted from the direct trust values in the past. If the direct trust values in the past are not available, a recommended trust value is established by mixing the opinions obtained from a number of so-called “experts”. These experts are trained to learn regions of different volatilities in a time series constructed from the recommended trust values. The dynamics between the experts and the mixing weights are obtained using a coarse-grain Hidden Markov Model.