Automated composition of Web Services can be achieved by using AI planning techniques. Hierarchical Task Network (HTN) planning is especially well-suited for this task. In this paper, we describe how HTN planning system SHOP2 can be used with OWL-S Web Service descriptions. We provide a sound and complete algorithm to translate OWL-S service descriptions to a SHOP2 domain. We prove the correctness of the algorithm by showing the correspondence to the situation calculus semantics of OWL-S. We implemented a system that plans over sets of OWL-S descriptions using SHOP2 and then executes the resulting plans over the Web. The system is also capable of executing information-providing Web Services during the planning process. We discuss the challenges and difficulties of using planning in the information-rich and human-oriented context of Web Services.

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. 1

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Reasoning capability is of crucial importance to many applications developed for the Semantic Web. Description Logics provide sound and complete reasoning algorithms that can effectively handle the DL fragment of the Web Ontology Language (OWL). However, existing DL reasoners were implemented long before OWL came into existence and lack some features that are essential for Semantic Web applications, such as reasoning with individuals, querying capabilities, nominal support, elimination of the unique name assumption and so forth. With these objectives in mind we have implemented an OWL DL reasoner and deployed it in various kinds of applications. 1

Abstract. Hierarchical Task-Network (HTN) based planning techniques have been applied to the problem of composing Web Services, especially when described using the OWL-S service ontologies. Many of the existing Web Services are either exclusively information providing or crucially depend on information-providing services. Thus, many interesting service compositions involve collecting information either during execution or during the composition process itself. In this paper, we focus on the latter issue. In particular, we present ENQUIRER, an HTN-planning algorithm designed for planning domains in which the information about the initial state of the world may not be complete, but it is discoverable through plan-time information-gathering queries. We have shown that ENQUIRER is sound and complete, and derived several mathematical relationships among the amount of available information, the likelihood of the planner finding a plan, and the quality of the plan found. We have performed experimental tests that confirmed our theoretical results and that demonstrated how ENQUIRER can be used in Web Service composition. 1

Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann’s state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.

In this paper, we present a general technique for taking forward-chaining planners for deterministic domains (e.g., HSP, TLPlan, TALplanner, and SHOP2) and adapting them to work in nondeterministic domains. Our results

This article gives an overview of AI (Artificial Intelligence) plan-ning techniques and discusses their application to the Web service composition problem.

This paper presents some enhancements in the temporal reasoning of a Hierarchical Task Network (HTN) planner, named SIADEX, that, up to authors knowledge, no other HTN planner has. These new features include a sound partial order metric structure, deadlines, temporal landmarking or synchronization capabilities built on top of a Simple Temporal Network and an efficient constraint propagation engine boosted by exploiting the causal structure of plans.

■ Automated planning technology has become mature enough to be useful in applications that range from game playing to control of space vehicles. In this article, Dana Nau discusses where automated-planning research has been, where it is likely to go, where he thinks it should go, and some major challenges in getting there. The article is an updated version of Nau’s invited talk at AAAI-05 in Pittsburgh, Pennsylvania. In ordinary English, plans can be many different kinds of things, such as project plans, pension plans, urban plans, and floor plans. In automated-planning research, the word refers specifically to plans of action: representations of future behavior... usually a set of actions, with temporal and other constraints on them, for execution by some agent or agents. 1 One motivation for automated-planning research is theoretical: planning is an important component of rational behavior—so if one objective of artificial intelligence is to grasp the computational aspects of intelligence, then certainly planning plays a critical role. Another motivation is very practical: plans are needed in many different fields of human endeavor, and in some cases it is desirable to create these plans automatically. In this regard, automatedplanning research has recently achieved several notable successes such as the Mars Rovers, software to plan sheet-metal bending operations,