Abstract
This note proposes a model predictive control (MPC) algorithm for the solution of a robust control problem for continuous-time systems. Discontinuous feedback strategies are allowed in the solution of the min-max problems to be solved. The use of such strategies allows MPC to address a large class of nonlinear systems, including among others nonholonomic systems. Robust stability conditions to ensure steering to a certain set under bounded disturbances are established. The use of bang-bang feedbacks described by a small number of parameters is proposed, reducing considerably the computational burden associated with solving a differential game. The applicability of the proposed algorithm is tested to control a unicycle mobile robot.

Abstract

Abstract
We present a meta-learning method to support selection of candidate learning algorithms. It uses a k-Nearest Neighbor algorithm to identify the datasets that are most similar to the one at hand. The distance between datasets is assessed using a relatively small set of data characteristics, which was selected to represent properties that affect algorithm performance. The performance of the candidate algorithms on those datasets is used to generate a recommendation to the user in the form of a ranking. The performance is assessed using a multicriteria evaluation measure that takes not only accuracy, but also time into account. As it is not common in Machine Learning to work with rankings, we had to identify and adapt existing statistical techniques to devise an appropriate evaluation methodology. Using that methodology, we show that the meta-learning method presented leads to significantly better rankings than the baseline ranking method. The evaluation methodology is general and can be adapted to other ranking problems. Although here we have concentrated on ranking classification algorithms, the meta-learning framework presented can provide assistance in the selection of combinations of methods or more complex problem solving strategies.

Abstract
Genericity is a topic which is not sufficiently developed in state-based systems modelling, mainly due to a myriad of approaches and behaviour models which lack unification. This paper adopts coalgebra theory to propose a generic notion of a state-based software component, and an associated calculus, by quantifying over behavioural models specified as strong monads. This leads to the pointfree, calculational reasoning style which is typical of the so-called Bird-Meertens school. ©2003 Published by Elsevier Science B. V.

Abstract
We describe a reference implementation of a multi-threaded run-time system for a core programming language based on a process calculus. The core language features processes running in parallel and communicating through asynchronous messages as the fundamental abstractions. The programming style is fully declarative, focusing on the interaction patterns between processes. The parallelism, implicit in the syntax of the programs, is effectively extracted by the language compiler and explored by the run-time system.

Abstract
Logic programming is based on the idea that computation is controlled inference. The Extended Andorra Model provides a very powerful framework that supports both co-routining and parallelism. In this work we show that David H. D. Warren's design for the EAM with Implicit Control does not perform well for deterministic computations and we present several optimisations that allow the BEAM to achieve performance matching or even exceeding related systems. Our optimisations refine the original EAM control rule demonstrate that overheads can be reduced through combined execution rules, and show that a good design and emulator implementation is relevant, even for a complex system such as the BEAM.