Sensor networks arise as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical system (MEMS) ease the development and proliferation of this kind of networks in a wide range of real-world applications. MultiAgent systems (MAS) have been identified as one of the most suitable technologies to contribute to this domain due to their appropriateness for modeling autonomous self-aware sensors in a flexible way. Firstly, this survey summarizes the actual challenges and research areas concerning sensor networks while identifying the most relevant MAS contributions. Secondly, we propose a taxonomy for sensor networks that classifies them depending on their features (and the research problems they pose). Finally, we identify some open future research directions and opportunities for MAS research.

Abstract. Ensemble classifiers combine the classification results of several classifiers.Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where incorrect modelling assumptions are possibly made, uniform averaging sometimes performs even better than bayesian model averaging. Linear mixtures over sets of models provide an space that includes uniform averaging as a particular case. We develop two algorithms for learning maximum a posteriori weights for linear mixtures, based on expectation maximization and on constrained optimizition. We provide a nontrivial example of the utility of these two algorithms by applying them for one dependence estimators.We develop the conjugate distribution for one dependence estimators and empirically show that uniform averaging is clearly superior to BMA for this family of models. After that we empirically show that the maximum a posteriori linear mixture weights improve accuracy significantly over uniform aggregation.

In this paper we propose a novel message-passing algorithm, the so-called Action-GDL, as an extension to the generalized distributive law (GDL) to efficiently solve DCOPs. Action-GDL provides a unifying perspective of several dynamic programming DCOP algorithms that are based on GDL, such as DPOP and DCPOP algorithms. We empirically show how Action-GDL using a novel distributed post-processing heuristic can outperform DCPOP, and by extension DPOP, even when the latter uses the best arrangement provided by multiple state-of-the-art heuristics.

Mixed Multi-Unit Combinatorial Auctions (MMUCAs) allow agents to bid for bundles of goods to buy, goods to sell, and transformations of goods. In particular, MMUCAs offer a high potential to be employed for the automated assembly of supply chains of agents offering goods and services, and in general MMUCAs extend and generalise several types of combinatorial auctions. Here we provide a formalism, based on an extension of Petri Nets, with which MMUCAs, and therefore all auction types subsumed by MMUCAs -and in particular combinatorial auctions for supply chain formation (SCF)-, can be formally analysed. As a second direct benefit, consequence of the provided mapping to Petri Nets, we manage to dramatically reduce the number of decision variables involved in the optimisation problem posed by MMUCAs from quadratic to linear for a wide class of MMUCA Winner Determination Problems (WDPs). Hence, we also make headway in the practical application of MMUCAs, and in particular to SCF.

Abstract. In this paper we report on a study on feature selection within the minimum-redundancy maximum-relevance framework. Features are ranked by their correlations to the target vector. These relevance scores are then integrated with correlations between features in order to obtain a set of relevant and least-redundant features. Applied measures of correlation or distributional similarity for redunancy and relevance include Kolmogorov-Smirnov (KS) test, Spearman correlations, JensenShannon divergence, and the sign-test. We introduce a metric called "value difference metric" (VDM) and present a simple measure, which we call "fit criterion" (FC). We draw conclusions about the usefulness of different measures. While KS-test and sign-test provided useful information, Spearman correlations are not fit for comparison of data of different measurement intervals. VDM was very good in our experiments as both redundancy and relevance measure. Jensen-Shannon and the sign-test are good redundancy measure alternatives and FC is a good relevance measure alternative.