ur approach, we use policy rules to specify government of collaborative behavior in multi-initiative collaborative services as opposed to modeling business processes. In particular, the composition policies are designed to enable dynamic linking of collaborative behavior. An area for which significant research has been done on composition is the area of aspect-oriented modeling (AOM) [30,31]. Both the AOM approach and our approach address cross-cutting concerns; however, we do this by modeling roles and using policy to bind them and define how they are to be composed, whereas in AOM, cross-cutting aspects are woven into so-called primary models using compo- sition directives. Although composition directives and policies play similar roles in that they both define ordering and coordination of composition, the AOM approach is more prescriptive and static in nature. Our work is different from the AOM approach in that we use policy and dynamic role-binding. Policy rules declare what may be composed depending on trigger events and conditions, and the decision to compose may occur at runtime depending on the dynamic behavior state of the system, allowing for a choice between different behaviors. Furthermore, we do not use patterns, pointcuts, and advice to specify composition. However, we specify more explicitly where, under what conditions, and how composition should occur. An approach to adaptive service composition with many similarities to our approach is presented in Funk [32]. Whereas they use a condition-action rule- based decision process, our policies are based on the event-condition-action para- digm. The decision system architecture is similar to our policy-based architecture, with a distributed management system. However, in Funk [32], the management is modeled more tightly integrated with the services than in our approach, where the policy management is specified separately from the individual services. 5.9 Conclusion In this chapter, we have presented a three-layered modeling approach to dynamic service c