Abstract
This paper proposes a model based on particle swarm optimization to aid electricity markets players in the selection of the best player(s) to trade with, to maximize their bilateral contracts outcome. This approach is integrated in a Decision Support System (DSS) for the pre-negotiation of bilateral contracts, which provides a missing feature in the state-of-art, the possible opponents analysis. The DSS determines the best action of all the actions that the supported player can take, by applying a game theory approach. However, the analysis of all actions can easily become very time-consuming in large negotiation scenarios. The proposed approach aims to provide the DSS with an alternative method with the capability of reducing the execution time while keeping the results quality as much as possible. Both approaches are tested in a realistic case study where the supported player could take almost half a million different actions. The results show that the proposed methodology is able to provide optimal and near-optimal solutions with an huge execution time reduction.

Abstract
The use of energy from renewable sources is one of the major concerns of today’s society. In recent years, the European Union has been changing legislation and implementing policies aimed at promoting its investment and encouraging its use in order to reduce the emission of greenhouse gases [1].

Abstract
The increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
This paper proposes the use of ontologies to enable information and knowledge exchange, to test different electricity market models and to allow players from different systems to interact in common market environments. Multi-agent based software is particularly well fitted to analyse dynamic and adaptive systems with complex interactions among its constituents, such as the complex and dynamic electricity markets. The main drivers are the markets' restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. An ontology to represent the concepts related to the Nord Pool Elspot market is proposed. It is validated through a case study considering the simulation of Elspot market. Results show that heterogeneous agents are able to effectively participate in the simulation by using the proposed ontologies to support their communications with the Nord Pool market operator.

Abstract
Artificial intelligence is transforming the way chatbots are created and used. The recent boom of artificial intelligence development is creating a whole new generation of intelligent approaches that enable a more efficient and effective design of chatbots. On the other hand, the increasing need and interest from the industry in artificial intelligence based solutions, is guaranteeing the necessary investment and applicational know-how that is pushing such solutions to a new dimension. Some relevant examples are e-commerce, health or education, which is the main focus of this work. This paper studies and analyses the impact that artificial intelligence models and solutions is having on the design and development of chatbots, when compared to the previously used approaches. Some of the most relevant current and future challenges in this domain are highlighted, which include language learning, sentiment interpretation, integration with other services, or data security and privacy issues.

Abstract
Electricity markets are complex and dynamic environments with very particular characteristics. Ambitious goals, including those set by the European Union, foster the increased use of distributed generation, essentially based on renewable energy sources. This requires major changes in electricity markets and energy systems, namely through the adoption of the smart grid paradigm. The use of simulation tools and the study of different market mechanisms and the relationships between their stakeholders are essential. One of the main challenges in this area is developing decision support tools to address the problem as a whole. This work contributes to increasing interoperability between heterogeneous systems, namely agent-based, directed to the study of electricity markets, the operation of smart grid, and energy management. To this end, this work proposes the use of ontologies to ease the interaction between entities of different natures and the use of semantic web technologies to develop more intelligent and flexible tools. A multiagent systems society, composed of several heterogeneous multiagent systems, which interact using the proposed ontologies, is presented as a proof-of-concept.