We address collaborative decision in the Multi-Agent Consistency-based online learning of relational action models. This framework considers a community of agents, each of them learning and rationally acting following their relational action model. It relies on the idea that when agents communicate, on a utility basis, the observed effect of past actions to other agents, this results in speeding up the online learning process of each agent in the community. In the present article, we discuss how collaboration in this framework can be extended to the individual decision level. More precisely, we first discuss how an agent's ability to predict the effect of some action in its current state is enhanced when it takes into account all the action models in the community. Secondly, we consider the situation in which an agent fails to produce a plan using its own action model, and show how it can interact with the other agents in the community in order to select an appropriate action to perform. Such a community aided action selection strategy will help the agent revise its action model and increase its ability to reach its current goal as well as future ones.