Abstract. Service interface description languages such as WSDL, and related standards, are evolving rapidly to provide a foundation for interoperation between Web services. At the same time, Semantic Web service technologies, such as the Ontology Web Language for Services (OWL-S), are developing the means by which services can be given richer semantic specifications. Richer semantics can enable fuller, more flexible automation of service provision and use, and support the construction of more powerful tools and methodologies. Both sets of technologies can benefit from complementary uses and crossfertilization of ideas. This paper shows how to use OWL-S in conjunction with Web service standards, and explains and illustrates the value added by the semantics expressed in OWL-S.
Current industry standards for describing Web Services focus on ensuring interoperability across diverse platforms, but do not provide a good foundation for automating the use of Web Services. Representational techniques being developed for the Semantic Web can be used to augment these standards. The resulting Web Service specifications enable the development of software programs that can interpret descriptions World Wide
Abstract. In this paper we present DAML-S, a DAML+OIL ontology for describing the properties and capabilities of Web Services. Web Services -Web-accessible programs and devices -are garnering a great deal of interest from industry, and standards are emerging for low-level descriptions of Web Services. DAML-S complements this effort by providing Web Service descriptions at the application layer, describing what a service can do, and not just how it does it. In this paper we describe three aspects of our ontology: the service profile, the process model, and the service grounding. The paper focuses on the grounding, which connects our ontology with low-level XML-based descriptions of Web Services.
Today's service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrow's successful service-oriented computing approaches.This article describes a 15-year roadmap for service-oriented multiagent system research. W e've already seen service-oriented computing (SOC) take hold in cross-enterprise business settings, such as the use of FedEx and UPS shipping services in e-commerce transactions; the aggregation of hotel, car rental, and airline services by Expedia and Orbitz; or bookrating services for libraries, consumers, and bookstores. Given the widespread interest in and deployment of Web services and service-oriented architectures that are occurring in industry, the scope of SOC in business settings will expand substantially. However, the emphasis has been on the execution of individual services and not on the more important problems of how services are selected and how they can collaborate to provide higher levels of functionality. Fortunately, four major trends in computing are addressing this problem:• Online ontologies are enabling meaning and understanding, arguably the last frontier for computing, to be captured and shared in more refined ways -via the Semantic Web initiative, for example, with the development of languages and representations for marking up heterogeneous content. In an alternative approach, shared representations are emerging from the works of (millions of) independent content developers. These ontologies will form models for numerous real-world entities and systems, as well as for the meanings of documents and content. • The widespread availability of many different types of sensors and effectors (including actuators and robotic devices) will enable online entities to not only become aware of the physical world, but also to manipulate, change, and control it.These trends are the new enablers that will drive SOC and multiagent system (MAS) research in the next decade and beyond. They portend an era in which complex systems will be modeled and simulated not just to understand them, but also to form predictions and interpretations that guide the monitoring and managing of them. SOC brings to the fore additional considerations, such as the necessity of modeling autonomous and heterogeneous components in uncertain and dynamic environments. Such components must be autonomously reactive and proactive yet able to interact flexibly with other components and environments. As a result, they're best thought of as agents, which collectively form MASs. Additionally, the key MAS concepts are reflected directly in those of SOC:• Multiagent SystemsThe history of MASs mirrors the history of computing in general. In the 1980s, distributed computing over LANs and advances in expert systems motivated the initial interest in distributed agents. Because the resulting systems functioned in single organizations, cooperation was the main focus. In the...
specification, management, conflict resolution, and enforcement To increase the assurance with which agents can be deployed in of policies within the specific contexts established by complex operational settings, we have been developing the KAoS policy organizational structures. Following a description of these and domain services. In conjunction with Nomads strong mobility capabilities (section 2), we will conclude with a brief summary of and safe execution features, KAoS services and tools allow for the current applications (section 3) and a brief outline of future specification, management, conflict resolution, and enforcement directions (section 4). of DAML-based policies within the specific contexts established by complex organizational structures. In this paper, we will KAoS AND NOMADS POLICY AND discuss results, issues, and lessons learned in the development of DOMAIN SERVICES these representations, tools, and services and their use in militaryKAoS is a collection of componentized agent services compatible and space applications with several popular agent frameworks, including Nomads [27], the DARPA CoABS Grid [18], the DARPA ALP/Ultra*Log
Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral sciences; expert and machine generated estimates of credibility; and, evidence of reproducibility, robustness, and replicability to validate the estimates. Beyond the primary research objective, the data and artifacts generated from this program will be openly shared and provide an unprecedented opportunity to examine research credibility and evidence.
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