In Multiagent Systems, agents need to coordinate their actions and plans with other agents, in order to reach their individual goals, which might be in conflict with others. Various methods like Markov Decision Processes or Distributed Constraint Satisfaction have been used to formally define the problem. More work has been dedicated to build systems that use interaction mechanisms inspired by biological systems, financial markets and game theory. These mechanisms utilize a range of communication tools from implicit cues to more complex languages for reaching agreements. A branch of research is focused on adaptive systems that learn to coordinate and successfully operate in changing environments. This review is a summary of some of the research on these approaches and applications in various problem domains.