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Semantic Web Service (SWS) composition is a challenging AI problem. We describe a theoretical and experimental framework based upon finite model search for constrained object models to address this problem. In many AI situations the input is rather simple, and the results complex to obtain. SWS composition requests themselves can turn very complex, and the problem of building these requests can be viewed as an AI problem of its own. This paper presents an operational end to end approach to composing/publishing Semantic Web Services involving two main reasoning stages. Composing is first performed at the abstract level of goals (each roughly representing a discovery request), which yields a composition request at the workflow level. The resulting worklow is finally processed to generate a valid publishable semantic web service. We present experimental results obtained on industrial use cases during the DIP project.

Abstract. Automatic or assisted workflow composition is a field of intense research for applications to the world wide web or to business process modeling. Workflow composition is traditionally addressed in various ways, generally via theorem proving techniques. Recent research [1] observed that building a composite workflow bears strong relationships with finite model search, and that some workflow languages can be defined as constrained object metamodels [2,3]. This lead to consider the viability of applying configuration techniques to this problem, which was proven feasible. Constrained based configuration expects a constrained object model as input. The purpose of this document is to formally specify the constrained object model involved in ongoing experiments and research using the Z specification language, and more precisely using some of the definitions in [4].

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Symmetries abound in logically formulated problems where many axioms are universally quantified, as this is the case in equational theories. Two complementary approaches have been used so far to dynamically tackle those symmetries: prediction and detection. The best-known predictive symmetry elimination method is the least number heuristic (lnh). A more recent predictive method, the extended least number heuristic (xlnh), focuses first on the enumeration of a bijection in the problem and easily exploits in the sequel the remaining isomorphisms. On the other hand, dynamic symmetry detection is costly in the general case (the problem is Graph Iso complete) but allows one to exploit more symmetries, and efficient (polytime) yet incomplete detection algorithms can be used on each node. This paper presents a generalization of xlnh that focuses on the enumeration of a unary function that does not require the function to be bijective, a general notion of symmetry for finite-model search in first-order logic together with an efficient symmetry detection algorithm, and a function-ordering heuristic that exploits the inherent structure of first-order logic theories to improve the search when using function-centric methods. A comprehensive study of the compared efficiency of all methods, in isolation and in combination, demonstrates the acceleration that can be expected in all cases.G. Audemard (B) CRIL, Université d'Artois, rue de l'université SP 16, These ideas are implemented by using the known system SEM as an experimentation framework, to allow for accurate comparisons.