Complex Automated Negotiations have been widely studied and are one of the emerging areas of research in the field of Autonomous Agents and Multi-Agent Systems. The complexity in an automated negotiation depends on several factors: the number of negotiated issues, dependencies between these issues, representation of the utility, negotiation protocol, negotiation form (bilateral or multi-party), time constraints, and so on. Complex automated negotiation scenarios are concerned with negotiation encounters where we may have for instance, a large number of agents, a large number of issues with strong interdependencies, real time constraints, etc. Many real world negotiation scenarios present one or more of the mentioned elements. Software agents can support the automation of complex negotiations, by negotiating on the behalf of their owners and providing adequate strategies to their owners to achieve realistic, win-win agreements. In order to provide solutions in such complex automated negotiation scenarios, research has focused on incorporating different technologies including search, CSP, graphical utility models, Bayesian nets, auctions, utility graphs, optimization and predicting and learning methods. The applications of complex automated negotiations could include e-commerce tools, decision-making support tools, negotiation support tools, collaboration tools, as well as knowledge discovery and agent learning tools.