The rising integration of pocket computing devices in our daily life duties has taken the attention of researchers from different scientific backgrounds. Today's amount of software applications that bring together advanced mobile services and literature of Artificial Intelligence (AI) is quite remarkable and worth investigating. Cooperation, coordination and negotiation are some of AI's focal points wherein many of its related research efforts are strengthening the join between sophisticated research outcomes and modern life requirements, such as serviceability on the move. In Distributed Artificial Intelligence (DAI), several of the research conducted in Multi-Agent Systems (MASs) addresses the mutually beneficial agreements that a group of interacting autonomous agents are expected to reach. In our research, we look at agents as the transportable software packets that each represents a set of needs a user of a pocket computing device demands from a remote service acquisition platform. However, when a set of software agents attempt to reach an agreement, a certain level of cooperation must be reached first, then, a negotiation process is carried out. Depending on each agent?s negotiation skills and considerations, the returns of each accomplished agreement can either be maximized or minimized. In this thesis, we introduce a new negotiation model, (i.e., protocol, set of tactics, strategy), for software agents to employ while attempting to acquire a service on behalf of users of pocket computing devices. The purpose of our model is to maximize the benefits of the interacting agents while considering the limitations of the communication technologies involved and, the nomadic nature of the users they represent. We show how our model can be generically implemented. Then, we introduce two case-studies that we have been working on with our industrial partner and, we demonstrate these cases' experimental results before and after applying our negotiation model.