This article presents a theoretical perspective on stuttering based on numerous findings regarding speech and nonspeech neuromotor control in individuals who stutter in combination with recent empirical data and theoretical models from the literature on the neuroscience of motor control. Specifically, this perspective on stuttering relies heavily on recent work regarding feedforward and feedback control schemes; the formation, consolidation, and updating of inverse and forward internal models of the motor systems; and cortical, subcortical, and cerebellar activation patterns during speech and nonspeech motor tasks. Against this background, we propose that stuttering may result when producing speech (a) with unstable or insufficiently activated internal models or (b) with a motor E

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This is a tutorial on logic programming and Prolog appropriate for a course on programming languages for students familiar with imperative programming.

Constraint programming offers facilities for problem modelling, constraint propagation and search. This paper discusses the resulting benefits for practical applications which exploit these facilities.The modelling facilities are particularly exploited in applications to verification, both of circuits and of real time control systems. The propagation facilities are exploited in applications involving user feedback and graphical interfaces. The search facilities are exploited in applications such as scheduling and resource allocation, which involve combinatorial problems.The paper surveys applications under each of these three headings.

This paper describes our experience in implementing an industrial application using the finite domain solver of the ECL i PS e constraint logic programming (CLP) system, in conjunction with the mathematical programming (MP) system, FortMP. In this technique, the ECL i PS e system generates a feasible solution that is adapted to construct a starting point (basic solution) for the MP solver. The basic solution is then used as an input to the FortMP system to warm-start the simplex (SX) algorithm, hastening the solution of the linear programming relaxation, (LPR). SX proceeds as normal to find the optimal integer solution. Preliminary results indicate that the integration of the two environments is suitable for this application in particular, and may generally yield significant benefits. We describe adaptations required in the hybrid method, and report encouraging experimental results for this problem.