Developing middleware services for dynamic distributed systems, e.g., ad-hoc networks, is a challenging task given that such services must deal with communicating devices that may join and leave the system, and fail or experience arbitrary delays. Algorithms developed for static settings are often not usable in dynamic settings because they rely on (logical) all-to-all connectivity or assume underlying routing protocols, which may be unfeasible in highly dynamic settings. This paper explores the indirect learning approach to information dissemination within a dynamic distributed data service. The indirect learning scheme is used to improve the liveness of the atomic read/write object service in the settings with uncertain connectivity. The service is formally proved to be correct, i.e., the atomicity of the objects is guaranteed in all executions. Conditional analysis of the performance of the new service is presented. This analysis has the potential of being generalized to other similar dynamic algorithms. Under the assumption that the network is connected, and assuming reasonable timing conditions, the bounds on the duration of the read/write operations of the new service are calculated. Finally, the paper proposes a deployment strategy where indirect learning leads to an improvement in communication costs relative to a previous solution.