A dynamic default revision mechanism for speculative computation

In this work a default revision mechanism is introduced into Speculative Computation to manage incomplete information. The default revision is supported by a method for the generation of default constraints based on Bayesian Networks. The method enables the generation of an initial set of defaults which is used to produce the most likely scenarios during the computation, represented by active processes. As facts arrive, the Bayesian Network is used to derive new defaults. The objective with such a new dynamic mechanism is to keep the active processes coherent with arrived facts. This is achieved by changing the initial set of default constraints during the reasoning process in Speculative Computation. A practical example in clinical decision support is described.info:eu-repo/semantics/publishedVersio

Strategic directions in constraint programming

An abstract is not available

A Scalable Linear Constraint Solver for User Interface Construction

Abstract. This paper proposes an algorithm for satisfying systems of linear equality and inequality constraints with hierarchical strengths or preferences. Basically, it is a numerical method that incrementally obtains the LU decompositions of linear constraint systems. To realize this, it introduces a novel technique for analyzing hierarchical systems of linear constraints. In addition, it improves performance by adopting techniques that utilize the sparsity and disjointness of constraint systems. Based on this algorithm, the HiRise constraint solver has been designed and implemented for the use of constructing interactive graphical user interfaces. This paper shows that HiRise is scalable up to thousands of simultaneous constraints in real-time execution.

Workshop On Computational Optimization

[No abstract available]29