Web services provide a standardized means of publishing diverse, distributed applications. Increasingly, corporations are providing services or programs within and between organizations either on corporate intranets or on the cloud. Many of these services can be composed together, ideally automatically, to provide value-added service. Automated Web service composition is an example of such automation where given a specification of an objective to be realized and some knowledge of the state of the world, the problem is to automatically select, integrate, and invoke multiple services to achieve the specified objective. A popular approach to the Web service composition problem is to conceive it as an Artificial Intelligence planning task. This enables us to bring to bear many of the theoretical and computational advances in reasoning about actions to the task of Web service composition. However, Web service composition goes far beyond the reaches of classical planning, presenting a number of interesting challenges relevant to a large body of problems related to the composition of actions, programs, and services. Among these, an important challenge is generating not only a composition, but a high-quality composition tailored to user preferences. In this thesis, we present an approach to the Web service composition problem with a particular focus on the customization of compositions. We claim that there is a correspondence between generating a customized composition of Web services and non-classical Artificial Intelligence planning where the objective of the planning problem is specified as a form of control knowledge, such as a workflow or template, together with a set of constraints to be optimized or enforced. We further claim that techniques in (preference-based) planning can provide a computational basis for the development of effective, state-of-the-art techniques for generating customized compositions of Web services. To evaluate our claim, we characterize the Web service composition problem with customization as a non-classical planning problem, exploit and advance preference specification languages and preference-based planning, develop algorithms tailored to the Web service composition problem, prove formal properties of these algorithms, implement proof-of-concept systems, and evaluate these systems experimentally. While our research has been motivated by Web services, the theory and techniques we have developed are amenable to analogous problems in such diverse sectors as multi-agent systems, business process modeling, component software composition, and social and computational behaviour modeling and verification.

Web services provide a standardized means of publishing diverse, distributed applications. Increasingly, corporations are providing services or programs within and between organizations either on corporate intranets or on the cloud. Many of these services can be composed together, ideally automatically, to provide value-added service. Automated Web service composition is an example of such automation where given a specification of an objective to be realized and some knowledge of the state of the world, the problem is to automatically select, integrate, and invoke multiple services to achieve the specified objective. A popular approach to the Web service composition problem is to conceive it as an Artificial Intelligence planning task. This enables us to bring to bear many of the theoretical and computational advances in reasoning about actions to the task of Web service composition. However, Web service composition goes far beyond the reaches of classical planning, presenting a number of interesting challenges relevant to a large body of problems related to the composition of actions, programs, and services. Among these, an important challenge is generating not only a composition, but a high-quality composition tailored to user preferences. In this thesis, we present an approach to the Web service composition problem with a particular focus on the customization of compositions. We claim that there is a correspondence between generating a customized composition of Web services and non-classical Artificial Intelligence planning where the objective of the planning problem is specified as a form of control knowledge, such as a workflow or template, together with a set of constraints to be optimized or enforced. We further claim that techniques in (preference-based) planning can provide a computational basis for the development of effective, state-of-the-art techniques for generating customized compositions of Web services. To evaluate our claim, we characterize the Web service composition problem with customization as a non-classical planning problem, exploit and advance preference specification languages and preference-based planning, develop algorithms tailored to the Web service composition problem, prove formal properties of these algorithms, implement proof-of-concept systems, and evaluate these systems experimentally. While our research has been motivated by Web services, the theory and techniques we have developed are amenable to analogous problems in such diverse sectors as multi-agent systems, business process modeling, component software composition, and social and computational behaviour modeling and verification.

Domain feature models concisely express commonality and variability among variants of a software product line. For separation of concerns, e.g., due to legal restrictions, technical considerations, and business requirements, multi-view approaches restrict the configuration choices on feature models for different stakeholders. However, recent approaches lack a formalization for precise, yet flexible specifications of views that ensure every derivable configuration perspective to obey feature model semantics. Here, we introduce a novel approach for clustering feature models to create multi-perspectives. Such customized perspectives result from composition of multiple concern-relevant views. A structured view model is used to organize feature groups, whereat a feature can be contained in multiple views. We provide formalizations for view composition and guaranteed consistency of the resulting perspectives w.r.t. feature model semantics. Thereupon, an efficient algorithm to verify consistency for entire clusterings is provided. We present an implementation and evaluate our concepts on two case studies.

Domain feature models concisely express commonality and variability among variants of a software product line. For separation of concerns, e.g., due to legal restrictions, technical considerations, and business requirements, multi-view approaches restrict the configuration choices on feature models for different stakeholders. However, recent approaches lack a formalization for precise, yet flexible specifications of views that ensure every derivable configuration perspective to obey feature model semantics. Here, we introduce a novel approach for clustering feature models to create multi-perspectives. Such customized perspectives result from composition of multiple concern-relevant views. A structured view model is used to organize feature groups, whereat a feature can be contained in multiple views. We provide formalizations for view composition and guaranteed consistency of the resulting perspectives w.r.t. feature model semantics. Thereupon, an efficient algorithm to verify consistency for entire clusterings is provided. We present an implementation and evaluate our concepts on two case studies.