Abstract
Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players' strategies to negotiate in the market. The proposed methodology is multi-agent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.

Abstract
Spreadsheets are notoriously error-prone. To help avoid the introduction of errors when changing spreadsheets, models that capture the structure and interdependencies of spreadsheets at a conceptual level have been proposed. Thus, spreadsheet evolution can be made safe within the confines of a model. As in any other model/instance setting, evolution may not only require changes at the instance level but also at the model level. When model changes are required, the safety of instance evolution can not be guarded by the model alone. We have designed an appropriate representation of spreadsheet models, including the fundamental notions of formulteand references. For these models and their instances, we have designed coupled transformation rules that cover specific spreadsheet evolution steps, such as the insertion of columns in all occurrences of a repeated block of cells. Each model-level transformation rule is coupled with instance level migration rules from the source to the target model and vice versa. These coupled rules can be composed to create compound transformations at the model level inducing compound transformations at the instance level. This approach guarantees safe evolution of spreadsheets even when models change.

Abstract
A suspended multicore fiber sensor for simultaneous measurement of curvature and strain is proposed. The spectral response shows evidences of several interferences arising from the seven cores of the fiber. Once the sensing head presents different sensitivities for curvature and strain measurements, these physical parameters can be discriminated by using the matrix method. The rms deviations are +/- 19m(-1) and +/- 12:90 mu epsilon for curvature and strain measurements, respectively. (C) 2011 Optical Society of America

Abstract
With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems.

Abstract
Markets today are characterized by enormous uncertainty in demand that combined with the increasing complexity of new products and competition, has forced companies to adopt new collaboration strategies with its business partners. SMEs need to form non-hierarchical networks where they can complement their capabilities and resources, achieve higher added value and higher control of the business. In these environments, decision making becomes decentralized and operations' planning presents new challenges and becomes more collaborative. An integrated solution for non-hierarchical networks collaborative planning is presented in this paper. The solution proposed includes two collaborative planning scenarios (aggregate and detailed), applied in different phases of non-hierarchical networks' lifecycle, which allows SME's to negotiate prices and delivery times in a collaboratively way. The solution considers requirements from companies belonging to three different industrial sectors, assuring its wide applicability. © 2011 Research Institute for Ope.

Abstract
This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.