KAoS Semantic Policy and Domain Services: An Application of DAML to Web Services-Based Grid Architectures M. Johnson, P. Chang, R. Jeffers, J. Bradshaw, M. Breedy, L. Bunch S. Kulkarni, J. Lott, N. Suri, A. Uszok Institute for Human and Machine Cognition (IHMC), University of West Florida, U.S.A. 40 S. Alcaniz Street, Pensacola, FL 32501, (850) 202-4462 {mjohnson, pchang, rjeffers, jbradshaw, mbreedy, lbunch skulkarni, jlott, nsuri, auszok}@ai.uwf.edu Von-Wun Soo Department of Computer Science, National Tsing Hua University 101, Section 2, Kuang Fu Road, Hsinchu, Taiwan 300, Republic of China, +886-3-5731068 soo@cs.nthu.edu.tw ABSTRACT This paper introduces a version of KAoS Semantic Policy and Domain Services that has been developed to support Web Services-based (i.e., OGSA-compliant) Grid Computing Architectures. While initially oriented to the dynamic and complex requirements of software agent applications, KAoS services are now being extended to work equally well with both agent and non-agent clients on a variety of more general distributed computing platforms. The OGSA-compliant version of KAoS services allows fine-grained policy-based management of registered Grid services as well as opening additional opportunities for the use of agents on the grid. Categories and Subject Descriptors I.2.11 [Artificial Intelligence] Distributed Artificial Intelligence - Intelligent agents, Multiagent systems I.2.4 [Artificial Intelligence] Knowledge Representation Formalisms and Methods - Representation languages, Semantic networks I.2.3 [Artificial Intelligence] Deduction and Theorem Proving - Inference engines General Terms Management, Security, Standardization, Languages Keywords policy, domain, access control, authorization, KAoS. Grid Computing, Web Services, DAML, OWL, Semantic Web, Semantic Grid, description logic, software agent 1. INTRODUCTION Despite rapid advances in computing technology, the demanding requirements of the science and business communities continue to outstrip available technology solutions. To help close this gap, both communities have attempted to more fully harness the power of sharing and coordinating the use of distributed resources. Since its inception in the mid-1990’s, Grid Computing has been focused on the need for a distributed computational infrastructure that supports resource sharing and coordinated problem solving across geographically distributed organizations [16]. Grid researchers envision people and resources from different institutions being gathered to form Virtual Organizations (VO) to address complex problems that require extensive collaboration and various instruments. Such VOs can grow to a very large size to encompass multiple institutions, each with their own sets of heterogeneous resources. Some successful research efforts consistent with this approach in the Grid Computing world are the NASA Information Power Grid (IPG) [33] and the EU DataGrid [34]. Recently, Grid Computing and Web Services have started to merge. The goal of the Web Services effort is to provide infrastructure for services to be advertised, found, and accessed over the Internet using Web-style protocols. Web Services and Grid Computing face many similar challenges: description and advertisement of services, description and matchmaking (lookup) of service requests, invocation of services (possibly with attached contracts regarding quality of service or other constraints), and the accounting mechanisms to charge for resource usage. Recognizing the overlap, the Globus group has announced the Open Grid Services Architecture (OGSA) [14] that brings Grid Computing services and Web Services infrastructures even closer together. OGSA defines a Grid service as a Web service that implements a set of standard WSDL port types that facilitate service lifetime management, service discovery, and other features [14]. Whereas Web Services will provide an infrastructure for Grid Computing, Grid Computing expands and enhances available Web Services in OGSA, providing a high performance infrastructure for demanding applications [9]. For example, while a typical Web transaction today involves only a small number of hosts, Grid Computing applications routinely incorporate large numbers of processes tightly interacting in a coordinated fashion. Early work in computational grids differed from agent-based systems in scope, features, capabilities, and target application domains, with the goals for Grid Computing being closer to traditional distributed systems. However, recent work on Semantic Grid [10; 27] and Semantic Web Services [11; 18; 28] architectures aims to support more dynamic, late-binding, and long-lived applications, making both platforms better-suited as platforms for multi-agent systems. Approaches such as semantic matchmaking that traditionally have been exclusively agent- oriented capabilities are also being incorporated. From the perspective of Semantic Web Services, the aim is to continue the transition of the Web from its initial role as a repository of text and images to a fully capable provider of services that can be effectively used not only by people but also