All promiment examples of first-order predicate fuzzy logics are undecidable. This leads to the problem of the arithmetical complexity of their sets of tautologies and satisfiable sentences. This paper is a contribution to the general study of this problem. We propose the classes of first-order core and ∆-core fuzzy logics as a good framework to address these arithmetical complexity issues. We ...

We consider the question of the adequacy of symbolic models versus computational models for the verification of security protocols. We neither try to include properties in the symbolic model that reflect the properties of the computational primitives nor add computational requirements that enforce the soundness of the symbolic model. We propose in this paper a different approach: everything is ...

This paper presents a kinematically consistent second-order computational homogenisation scheme for shear deformable shells. The proposed framework can accurately evaluate the membrane, bending, and transverse components of shell resultants tangent operators, whilst showing no size dependency on fine scale model. To date, proper extension to thick model, such as 5-parameter formulation, remains...

service oriented architecture (soa) technology enables composition of large and complex computational units out of the available atomic services. implementation of soa brings about challenges which include service discovery, service interaction, service composition, robustness, quality of service, security, etc. these challenges are mainly due to the dynamic nature of soa. soamay often need to ...

Computational social choice literature has successfully studied the complexity of manipulation in various voting systems. However, the existing models of coalitional manipulation view the manipulating coalition as an exogenous input, ignoring the question of the coalition formation process. While such analysis is useful as a first approximation, a richer framework is required to model voting ma...

Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems. Probability is the bestunderstood and most widely applied formalism for computational scientific reasoning under uncertainty. Increasing...