Abstract
In this paper we relax the Colonel Blotto game assumption that for a given battle the player who allocates the higher measure of resources wins that battle. We assume that for a given battle, the Colonel who allocates the higher measure of resources is more likely to win. We have a simpler model for which we are able to compute all Nash equilibria in pure strategies for any valuations profile that players might have, something that is not possible for the original Blotto game.

Abstract
Multi-agent systems are a suitable approach to address the current requirements imposed to companies demanding for more flexible, adaptive and reconfigurable systems. In spite of the promising perspective, only a reduced number of such solutions are running in industrial scenarios. Standardization is recognized as a major issue in the development of devices, systems or processes in industrial environments, constituting a real road-blocker for the wider acceptance of agent-based solutions in industry. This paper discusses the importance of standardization compliance in the development of industrial agent-based solutions and the actions to be performed to overcome this issue.

Abstract
Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.

Abstract
Nowadays, manufacturing control systems have evolved from reactive and informative decision support system to a proactive and intelligent manufacture management mechanism. Industries require both optimal and adaptive manufacturing processes in order to respond competitively to market requirements. In response, advanced manufacturing control systems are configured as artificial intelligence distributed architectures capable to support environment disturbances. However, these techniques, specifically Multi-Agent systems and Holonic Manufacturing Systems, are weak supporting optimal performance. Conversely, Operational Research decision support systems achieve optimality under centralized architectures. Still, these are weak supporting adaptable processes under environmental disturbances. Consequently, researchers recently have focused in articulating optimality and adaptation paradigms in order to construct a robust optimal-wise and adaptable mechanism, constructs a proposed typology according structural features and discusses future possibilities for balancing optimality and adaptability characteristics.

Abstract
Image classification techniques are often used to reduce the large data volume content of an image to a simplified version - a thematic map, which can be more suitable from the user's point of view. However, the delimitation of specific regions using unsupervised classification techniques frequently generates an excessive number of clusters or classes. The resulting image can be simplified by a process of hierarchical aggregation of the initial classes, yielding a set of classified images. This set of thematic maps can provide a powerful insight into the image content, as long as an adequate visualization strategy is used. This paper presents methodologies for the visualization of hierarchically structured classified images.

Abstract