Negotiation among computational autonomous agents has gained rapidly growing interest in previous years, mainly due to its broad application potential in many areas such as e-commerce and e-business. This work deals with automated bilateral multi-issue negotiation in complex environments. Although tremendous progress has been made, available algorithms and techniques typically are limited in their applicability for more complex situations, in that most of them are based on simplifying assumptions about the negotiation complexity such as simple or partially known opponent behaviors and availability of negotiation history. We propose a negotiation approach called OMAC* that aims at tackling these problems. OMAC* enables an agent to efficiently model opponents in real-time through discrete wavelet transformation and non-linear regression with Gaussian processes. Based on the approximated model the decision-making component of OMAC* adaptively adjusts its utility expectations and negotiation moves. Extensive experimental results are provided that demonstrate the negotiation qualities of OMAC*, both from the standard mean-score performance perspective and the perspective of empirical game theory. The results show that OMAC* outperforms the top agents from the 2012, 2011 and 2010 International Automated Negotiating Agents Competition (ANAC) in a broad range of negotiation scenarios.