Preference-based Web Service Composition: A Middle Ground Between Execution and Search
Much of the AI-related work on Web Service Composition (WSC) relates it to an Artificial Intelligence (AI) planning problem, where the composition is primarily done offline prior to execution. Recent research on WSC has argued convincingly for the importance of optimizing quality of service and user preferences. While some of this optimization can be done offline, many interesting and useful optimizations are data-dependent, and must be done following execution of at least some information-providing services. In this paper, we examine this class of WSC problems, attempting to bridge the gap between offline composition and online information gathering with a view to producing high-quality compositions without excessive data gathering. Our investigation is performed in the context of an existing preference-based Hierarchical Task Networks (HTNs) WSC system. Our experiments show an improvement in both the quality and speed of finding a composition.