Planning and monitoring the execution of web service requests

Interaction with web services enabled marketplaces would be greatly facilitated if users were given a high level service request language to express their goals in complex business domains. This could be achieved by using a planning framework which monitors the execution of planned goals against predefined standard business processes and interacts with the user to achieve goal satisfaction. We present a planning architecture that accepts high level requests, expressed in XSRL (Xml Service Request Language). The planning framework is based on the principle of interleaving planning and execution. This is accomplished on the basis of refinement and revision as new service-related information is gathered from UDDI and web services instances, and as execution circumstances necessitate change. The system interacts with the user whenever confirmation or verification is needed

XSRL: An XML web-services request language

One of the most serious challenges that web-service enabled e-marketplaces face is the lack of formal support for expressing service requests against UDDI-resident web-services in order to solve a complex business problem. In this paper we present a web-service request language (XSRL) developed on the basis of AI planning and the XML database query language XQuery. This framework is designed to handle and execute XSRL requests and is capable of performing planning actions under uncertainty on the basis of refinement and revision as new service-related information is accumulated (via interaction with the user or UDDI) and as execution circumstances necessitate change

Automated synthesis of executable web service compositions from BPEL4WS processes

We propose a technique for the automated synthesis of new com-posite web services. Given a set of abstract BPEL4WS descriptions of component services, and a composition requirement, we auto-matically generate a concrete BPEL4WS process that, when exe-cuted, interacts with the components and satisfies the requirement. We implement the proposed approach exploiting efficient repre-sentation techniques, and we show its scalability over case stud-ies taken from a real world application and over a parameterized domain

The Planning Spectrum - One, Two, Three, Infinity

Linear Temporal Logic (LTL) is widely used for defining conditions on the execution paths of dynamic systems. In the case of dynamic systems that allow for nondeterministic evolutions, one has to specify, along with an LTL formula f, which are the paths that are required to satisfy the formula. Two extreme cases are the universal interpretation A.f, which requires that the formula be satisfied for all execution paths, and the existential interpretation E.f, which requires that the formula be satisfied for some execution path. When LTL is applied to the definition of goals in planning problems on nondeterministic domains, these two extreme cases are too restrictive. It is often impossible to develop plans that achieve the goal in all the nondeterministic evolutions of a system, and it is too weak to require that the goal is satisfied by some execution. In this paper we explore alternative interpretations of an LTL formula that are between these extreme cases. We define a new language that permits an arbitrary combination of the A and E quantifiers, thus allowing, for instance, to require that each finite execution can be extended to an execution satisfying an LTL formula (AE.f), or that there is some finite execution whose extensions all satisfy an LTL formula (EA.f). We show that only eight of these combinations of path quantifiers are relevant, corresponding to an alternation of the quantifiers of length one (A and E), two (AE and EA), three (AEA and EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for the new language that is based on an automata-theoretic approach, and study its complexity

Taming Numbers and Durations in the Model Checking Integrated Planning System

The Model Checking Integrated Planning System (MIPS) is a temporal least commitment heuristic search planner based on a flexible object-oriented workbench architecture. Its design clearly separates explicit and symbolic directed exploration algorithms from the set of on-line and off-line computed estimates and associated data structures. MIPS has shown distinguished performance in the last two international planning competitions. In the last event the description language was extended from pure propositional planning to include numerical state variables, action durations, and plan quality objective functions. Plans were no longer sequences of actions but time-stamped schedules. As a participant of the fully automated track of the competition, MIPS has proven to be a general system; in each track and every benchmark domain it efficiently computed plans of remarkable quality. This article introduces and analyzes the most important algorithmic novelties that were necessary to tackle the new layers of expressiveness in the benchmark problems and to achieve a high level of performance. The extensions include critical path analysis of sequentially generated plans to generate corresponding optimal parallel plans. The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations. The efficiency of this algorithm also allows us to improve the exploration guidance: for each encountered planning state the corresponding approximate sequential plan is scheduled. One major strength of MIPS is its static analysis phase that grounds and simplifies parameterized predicates, functions and operators, that infers knowledge to minimize the state description length, and that detects domain object symmetries. The latter aspect is analyzed in detail. MIPS has been developed to serve as a complete and optimal state space planner, with admissible estimates, exploration engines and branching cuts. In the competition version, however, certain performance compromises had to be made, including floating point arithmetic, weighted heuristic search exploration according to an inadmissible estimate and parameterized optimization