Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

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.

Using multimedia concept maps to enhance the learner experience in business law

Students’ learning experience can be affected by difficulties in understanding the interrelationships between concepts and also between topics. Concept maps have been used in many disciplines (Kremer & Gains, 1996) to structure information and express relationships between them. Their holistic approach with multiple pathways through the learning resource can make relationships and linkages between topics and subtopics obvious, and contribute to a meaningful and positive learning experience. This paper outlines the development and formative evaluation of two hypermedia concept maps which led to the development of a series of eleven concept maps to enhance the learning experience of students in a first year undergraduate business law unit.

As part of the Stage 1 formative evaluation, two concept maps were developed together with supporting multimedia resources and trialled on the learners. Feedback was also obtained from technical staff. This phase was designed to assess and control the quality of the learning resource as well as the impact it had on the learning experience. The paper closes by discussing how information gained in Stage 1 was used in Stage 2 as a basis to modify the initially trialled maps and to develop the other supporting maps.

Abordagem de construção de arquitetura homogênea para comitês via meta-aprendizagem

In the world we are constantly performing everyday actions. Two of these actions are frequent and of great importance: classify (sort by classes) and take decision. When we encounter problems with a relatively high degree of complexity, we tend to seek other opinions, usually from people who have some knowledge or even to the extent possible, are experts in the problem domain in question in order to help us in the decision-making process. Both the classification process as the process of decision making, we are guided by consideration of the characteristics involved in the specific problem. The characterization of a set of objects is part of the decision making process in general. In Machine Learning this classification happens through a learning algorithm and the characterization is applied to databases. The classification algorithms can be employed individually or by machine committees. The choice of the best methods to be used in the construction of a committee is a very arduous task. In this work, it will be investigated meta-learning techniques in selecting the best configuration parameters of homogeneous committees for applications in various classification problems. These parameters are: the base classifier, the architecture and the size of this architecture. We investigated nine types of inductors candidates for based classifier, two methods of generation of architecture and nine medium-sized groups for architecture. Dimensionality reduction techniques have been applied to metabases looking for improvement. Five classifiers methods are investigated as meta-learners in the process of choosing the best parameters of a homogeneous committee.