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Background: Service Composition is an important feature of Service Oriented Computing, yet it remains mostly a manual process. Given the dynamic and decentralized nature of services, manual composition is a complex undertaking. Proposals to automate this process exist, but suffer from practical problems that hinder their implementation. Methods: In this paper, we introduce a pragmatic approach where we reverse engineer a service composition repository to obtain the necessary information for automated solutions to work. We then evaluate the quality of the automated compositions based on their similarity to the ones written manually. A classic planning algorithm was adapted in order to generate solutions closer to those expected by developers. Results: The use of classical planning tools is too time-consuming for agile development scenarios. A simplified, tailored implementation can be orders of magnitude faster than a generic planner, which suggests that expressive power may need to be sacrificed in favor of usability. Our evaluation showed that ensuring the adherence of the solution to the initial specification by enforcing the use of all input parameters was capable of significantly increasing the quality of the solutions. Conclusions: It is possible to increase the quality of automated composition by applying planning algorithms specially crafted for the service composition task. Comparisons with automated planning tools highlight the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

Research in automated planning provides novel insights into service composition and contributes towards the provision of automatic compositions which adapt to changing user needs and environmental conditions. Most of the existing planning approaches to aggregating services, however, suffer from one or more of the following limitations: they are not domain-independent, cannot efficiently deal with numeric-valued variables, especially sensing outcomes or operator inputs, and they disregard recovery from runtime contingencies due to erroneous service behavior or exogenous events that interfere with plan execution. We present the RuGPlanner, which models the planning task as a Constraint Satisfaction Problem. In order to address the requirements put forward by service domains, the RuGPlanner is endowed with a number of special features. These include a knowledge-level representation to model uncertainty about the initial state and the outcome of sensing actions, and efficient handling of numeric-valued variables, inputs to actions or observational effects. In addition, it generates plans with a high level of parallelism, it supports a rich declarative language for expressing extended goals, and allows for continual plan revision to deal with sensing outputs, failures, long response times or timeouts, as well as the activities of external agents. The proposed planning framework is evaluated based on a number of scenarios to demonstrate its feasibility and efficiency in several planning domains and execution circumstances which reflect concerns from different service environments. [ABSTRACT FROM AUTHOR]

Hierarchies are one of the most common structures used to understand and conceptualise the world. Within the field of Artificial Intelligence (AI) planning, which deals with the automation of world-relevant problems, Hierarchical Task Network (HTN) planning is the branch that represents and handles hierarchies. In particular, the requirement for rich domain knowledge to characterise the world enables HTN planning to be very useful, and also to perform well. However, the history of almost 40 years obfuscates the current understanding of HTN planning in terms of accomplishments, planning models, similarities and differences among hierarchical planners, and its current and objective image. On top of these issues, the ability of hierarchical planning to truly cope with the requirements of real-world applications has been often questioned. As a remedy, we propose a framework-based approach where we first provide a basis for defining different formal models of hierarchical planning, and define two models that comprise a large portion of HTN planners. Second, we provide a set of concepts that helps in interpreting HTN planners from the aspect of their search space. Then, we analyse and compare the planners based on a variety of properties organised in five segments, namely domain authoring, expressiveness, competence, computation and applicability. Furthermore, we select Web service composition as a real-world and current application, and classify and compare the approaches that employ HTN planning to solve the problem of service composition. Finally, we conclude with our findings and present directions for future work. In summary, we provide a novel and comprehensive viewpoint on a core AI planning technique. [ABSTRACT FROM AUTHOR]

Web service composition is a significant problem as the number of available web services increases; however, manual composition is not an efficient option. Automated web service composition can be performed using AI Planning techniques, utilizing descriptions of available atomic web services, enhanced with semantic awareness and relaxation. This paper discusses a unified, semantically aware approach, handling both semantic (OWL-S & SAWSDL) and non-semantic (WSDL) web service descriptions. In the first case, ontology analysis is adopted to semantically enhance the planning domains and problems, in order to deal with cases where exact syntactic input-to-output matching is not feasible. In the non-semantic descriptions case, semantic information is acquired utilizing alternative sources such as lexical thesauri. Concept similarity measures are applied and utilized to achieve the desired degree of semantic relaxation. The solution to a web service composition problem is a plan describing the desired composite service. To support the proposed approach, the PORSCE framework has been implemented. The framework is modular, integrating discrete web service description languages and semantic relaxation techniques. Based on the similarity measures suggested in the paper, performance issues are also explored. [ABSTRACT FROM AUTHOR]

With the rising popularity of Web services due to their potential to enable interoperability between applications implemented on different platforms, new issues far more complex and crucial than interoperability have appeared, related mainly to Web services composition. Indeed assuming that for each service request there exists a single Web service that perfectly satisfies it on its own is rather unrealistic, in many cases composing functionalities offered by different services may be needed to satisfy a client request. However, current processes of Web services composition tend to be static in the sense that such processes are normally generated off-line and manual as it requires human intervention to be managed. Therefore in order to give value added customized services to users, recognition is growing of the need for automation and dynamicity to be taking in consideration while designing systems supporting Web services integration. Since automated dynamic composition process allows to reduce the time spent in building composition schema, to eliminate human errors and to reduce the overall cost of the process. Thus our research contribution lies in the area of automated dynamic composition of Web services. We propose a conceptual approach to compose Web services by combining the Semantic Web technologies, Artificial Intelligent Planning methods, Multi-Agent architecture and Self-healing propriety of Autonomic Computing paradigm. [ABSTRACT FROM AUTHOR]

Geospatial web services are widely utilised to disseminate spatial data and processing capability over the internet. Combining geospatial web services and generating composite web services out of atomic services can provide users with a higher level of functionality. This paper presents a solution for the automatic composition of geospatial web services. The solution relies on semantic web services technology and Artificial Intelligence (AI) planning techniques to generate composite geospatial web services. Web Service Modelling Ontology (WSMO) is selected as the underlying semantic web service framework. A neoclassical planning algorithm called Graphplan is used as the AI planner. The applicability of the proposed solution for composing web services is illustrated in a case study. The result shows that the proposed approach is well suited for the automatic composition of web services. The future requirements for adaptation of the approach in real world problems are broadly described. [ABSTRACT FROM AUTHOR]

Highlights: [•] An automatic solution to compose OWSs (OGC web services). [•] OWSs are semantically annotation using RDF and SPARQL. [•] Artificial Intelligence planning is used to compose OWSs. [•] The proposed solution is evaluated through a case-study in evacuation sheltering. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach based on the planning-graph to solve the top-k QoS-aware automatic composition problem of semantic Web services. The approach includes three sequential stages: a forward search stage to generate a planning-graph to reduce the search space of the following two stages greatly, an optimal local QoS calculating stage to compute all the optimal local QoS values of services in the planning, and a backward search stage to find the top-K composed services with optimal QoS values according to the planning-graph and the optimal QoS value. In order to validate the approach, experiments are carried out based on the test sets offered by the WS-Challenge competition 2009. The results show that the approach can find the same optimal solution as the champion system from the competition but also can provide more alternative solutions with the optimal QoS for users. [ABSTRACT FROM AUTHOR]

Description Logic possesses strong knowledge representation and reasoning capabilities and offers logical foundation for Semantic Web ontology languages such as OWL and OWL-S. However, the present implementations of OWL and OWL-S are deficient in semantic modeling for dynamic service composition. They also do not consider user preferences. AI planning possesses a better modeling capability of action state transformations in planning and provides an effective method for solving planning problem and task decomposition. However, it is limited in knowledge representation and reasoning capabilities. Based on merits of Description Logic, OWL-S and AI planning, this paper extends OWL-S model, proposes a service composition mechanism and testifies its feasibility in Description Logic. The results show that this composition mechanism is not only feasible but also helpful for semantic modeling of service composition in Semantic Web. [ABSTRACT FROM AUTHOR]

In this paper, novel ideas are presented for solving the automated web service composition problem. Some of the possible real world problems such as partial observability of the environment, nondeterministic effects of web services and service execution failures are solved through a dynamic planning approach. The proposed approach is based on a novel AI planner that is designed for working in highly dynamic environments under time constraints, namely Simplanner. World altering service calls are done according to the WS-Coordination and WS-Business Activity web service transaction specifications in order to physically recover from failure situations and prevent the undesired side effects of the aborted web service composition efforts. [ABSTRACT FROM AUTHOR]

In a Web service-based distributed environment, individual services must be chained together dynamically to solve a complex real world problem. The Semantic Web Service has shown promise for automatic chaining of Web services. This paper addresses semi-automatic geospatial service chaining through Semantic Web Services-based process planning. Process planning includes three phases: process modeling, process model instantiation and workflow execution. Ontologies and Artificial Intelligence (AI) planning methods are employed in process planning to help a user dynamically create an executable workflow for earth science applications. In particular, the approach was implemented in a common data and service environment enabled by interoperable standards from OGC and W3C. A case study of the chaining process for wildfire prediction illustrates the applicability of this approach. [ABSTRACT FROM AUTHOR]