Abstract
Network coding has the potential to enhance energy efficiency of multicast sessions by providing optimal communication subgraphs for the transmission of the data. However, the coding requirement at intermediate nodes may introduce additional complexity and energy consumption in order to code the data packets. Previous work has shown that in lossless wireline networks, the performance of tree-packing mechanisms is comparable to network coding, albeit with added complexity at the time of computing the trees. This means that most nodes in the network need not code. Thus, mechanisms that identify intermediate nodes that do require coding is instrumental for the efficient operation of coded networks and can have a significant impact in overall energy consumption. We present a distributed, low complexity algorithm that allows every node to identify if it should code and, if so, through what output link should the coded packets be sent. Our algorithm uses as input the optimal subgraph determined by Lun et al's optimization formulation [13]. Numerical results are provided using common Internet Service Provider (ISP) network topologies and also random network deployments. Our results show that the number of coding nodes in the expectation is very low (typically below 1) and that the number of sessions that require coding is limited, e.g., less than 15% for sessions of 4 receivers for the ISP networks and below 0.1% for networks with random node deployments in a square of 1 × 1 km2 with of up to 30 nodes and up to 20 receivers. © 2013 IEEE.

Abstract
Artificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players' profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paperscissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market. © Springer International Publishing Switzerland 2013.

Abstract
Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system's particular ontology must be extended from this top-level ontology. © Springer International Publishing Switzerland 2013.

Abstract
Recent changes in electricity markets (EMs) have been potentiating the globalization of distributed generation. With distributed generation the number of players acting in the EMs and connected to the main grid has grown, increasing the market complexity. Multi-agent simulation arises as an interesting way of analysing players' behaviour and interactions, namely coalitions of players, as well as their effects on the market. MASCEM was developed to allow studying the market operation of several different players and MASGriP is being developed to allow the simulation of the micro and smart grid concepts in very different scenarios This paper presents a methodology based on artificial intelligence techniques (AI) for the management of a micro grid. The use of fuzzy logic is proposed for the analysis of the agent consumption elasticity, while a case based reasoning, used to predict agents' reaction to price changes, is an interesting tool for the micro grid operator. © 2013 IEEE.

Abstract
With increasing penetration of electricity application in society and the need of majority of appliance to electricity, high level of reliability becomes more essential; in one hand with deregulation of electricity market in production, transmission and distribution and emerge of competitive electricity markets and in the other hand with increasing penetration of Distributed Generation (DG) because of environment issues and diminishing in fossil fuel reserves and its price growth, made microgrid more attractive. Micro grids are considers as partial of SmartGrid system to accommodate DGs as well as control, protection and operation systems for electrical equipment to connect generation to consumption in better and more reliable way to maintain adequate operation system in distribution level. A highly challenging issue in Microgrid is protection scheme, which needs to develop and modify. This paper proposes a new approach for protection in a Microgrid environment as a part of SmartGrid: Multi-agent system to Protections Coordination (MAS-ProteC) which integrated in MASGriP (Multi-Agent Smart Grid Platform), providing protection services within network operation in SmartGrid in electricity market context. © 2013 IEEE.

Abstract
The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players' actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets' negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets' players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets' data, using MASCEM - a multi-agent electricity market simulator that simulates market players' operation in the market.