We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in dynamic, uncertain, real-time, and noisy environments. Our strategy focuses on multi-issue negotiations where each issue is a request from the initiating agent to the responding agent. The initiating agent conducts multiple concurrent negotiations with responding agents and in each negotiation it employs (1) a pipelined, one-at-a-time approach, or (2) a confidence-based, packaged approach. In the former, lacking knowledge on the responding agent, it negotiates one issue at a time. In the latter, with confident knowledge of the past behavior of the responding agent, it packages multiple issues into the negotiation. We incorporate this adaptive strategy into a multi-phase coalition formation model (MPCF) in which agents learn to form coalitions and perform global tasks. The MPCF model consists of three phases: coalition planning, coalition instantiation and coalition evaluation. In this paper, we focus on the instantiation phase where the negotiations take place.