Applying Semantic Web Service Composition for Action Planning in Multi-robot Systems

Trust and Privacy Enabled Service Composition Using Social Experience

“…The proposed framework is an extension of our previous work [2] by incorporating components dealing with trustworthiness of services and profiling of user experience with service. The architecture of the framework is depicted in Fig.3.…”

“…Based on this observation, we justify our privacy inference model. Let's presume availability of two nodes (individuals) s and u in our target social network and consider p s as given privacy value to profile of individual s. Thus, inferred privacy rating of node s from perspective of node u can be computed by formula (2).…”

“…For every node sink in the network, first, the inferred trust towards src is computed using TrustInference procedure which could implement any of the aforementioned trust inference models. Then the respective inferred privacy of node sink towards src is measured by PrivacyInference procedure that implements the privacy model presented in formula (2). If the inferred privacy value is greater than maximum tolerable privacy threshold, then the profile content is not visible and the node will be marked as intermediate node.…”

“…The requirement expansion is performed according to the method proposed by Kungas and Dumas [11]. B) Problem Decomposition Layer: The objective of this layer is discovery of potential services in the problem domain that could realize end-user expanded requirement [2]. C) Service Composition Engine Layer: The goal of this layer is generation of a plan (plans) to fulfill the user requirement through composition of discovered services [2].…”

“…B) Problem Decomposition Layer: The objective of this layer is discovery of potential services in the problem domain that could realize end-user expanded requirement [2]. C) Service Composition Engine Layer: The goal of this layer is generation of a plan (plans) to fulfill the user requirement through composition of discovered services [2]. D) Trust & Privacy Inference Engine: It accommodates the trust and privacy inference algorithms and implements network pruning algorithm to compute the trustworthiness of a service from a specific user's perspective (Sections 2.3, and 2.4).…”