ISEP - GECAD - Grupo de Investigação em Engenharia do Conhecimento a Apoio à Decisão
Permanent URI for this community
GECAD is a research unit settled in the Institute of Engineering - Polytechnic of Porto (ISEP/IPP) having as mission the promotion and development of scientific research in the Knowledge and Decision Sciences domains, having Information Technologies as support. It involves 2 research groups: Intelligent Systems and Power Energy Systems. GECAD is known worldwide in its areas of research, leading some research domains. [-]
GECAD is coordinated by Prof. Zita Vale, and recognized by FCT (Portuguese Science and Technology Foundation). 79 researchers are involved in GECAD, including 37 with PhD degree. It is the largest R&D unit from the Polytechnic sub-system of Portugal.
GECAD was involved in more than 60 R&D projects (more than 20 on-going projects now) with external funding. We are one of the Portuguese R&D units with more success at this level. Just a number, 8, is the number of projects assigned to GECAD in the last FCT Call for Projects. GECAD has a tremendous success in publications in important scientific journals; many special issues of these journals are edited by GECAD researchers.
Understanding its responsibility for the Society development, GECAD has decided to adopt a new slogan: “Intelligence for a Sustainable, Safe, and Inclusive World”. For this reason, the most recent GECAD projects are applied to areas like Energy, Transportation, Environment, Economy, Inclusion, Critical Infrastructures, Security, Information Access and new ways of Socialization.
Browse
Browsing ISEP - GECAD - Grupo de Investigação em Engenharia do Conhecimento a Apoio à Decisão by Title
Now showing 1 - 10 of 822
Results Per Page
Sort Options
- 2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and resultsPublication . Lezama, Fernando; Soares, João; Vale, Zita; Rueda, Jose; Rivera, Sergio; Elrich, IstvánThis paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems.
- ABS4GD: a multi-agent system that simulates group decision processes considering emotional and argumentative aspectsPublication . Marreiros, Goreti; Santos, Ricardo; Ramos, Carlos; Neves, José; Bulas-Cruz, JoséEmotion although being an important factor in our every day life it is many times forgotten in the development of systems to be used by persons. In this work we present an architecture for a ubiquitous group decision support system able to support persons in group decision processes. The system considers the emotional factors of the intervenient participants, as well as the argumentation between them. Particular attention will be taken to one of components of this system: the multi-agent simulator, modeling the human participants, considering emotional characteristics, and allowing the exchanges of hypothetic arguments among the participants.
- Adaptable control for electrical generation at irregural wind speedsPublication . Puga, Ricardo; Ferreira, Judite; Cunha, J. Boaventura; Vale, ZitaThe main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.
- Adaptation model for PCMAT – Mathematics collaborative learning platformPublication . Fernandes, Marta; Martins, Constantino; Faria, Luiz; Couto, Paulo; Valente, Cristiano; Bastos, Cristina; Costa, Fátima; Carrapatoso, EuricoThe aim of this paper is to present an adaptation model for an Adaptive Educational Hypermedia System, PCMAT. The adaptation of the application is based on progressive self-assessment (exercises, tasks, and so on) and applies the constructivist learning theory and the learning styles theory. Our objective is the creation of a better, more adequate adaptation model that takes into account the complexities of different users.
- Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion DetectionPublication . Vitorino, João; Oliveira, Nuno; Praça, IsabelAdversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially concerning. Nonetheless, an example generated for a domain with tabular data must be realistic within that domain. This work establishes the fundamental constraint levels required to achieve realism and introduces the Adaptative Perturbation Pattern Method (A2PM) to fulfill these constraints in a gray-box setting. A2PM relies on pattern sequences that are independently adapted to the characteristics of each class to create valid and coherent data perturbations. The proposed method was evaluated in a cybersecurity case study with two scenarios: Enterprise and Internet of Things (IoT) networks. Multilayer Perceptron (MLP) and Random Forest (RF) classifiers were created with regular and adversarial training, using the CIC-IDS2017 and IoT-23 datasets. In each scenario, targeted and untargeted attacks were performed against the classifiers, and the generated examples were compared with the original network traffic flows to assess their realism. The obtained results demonstrate that A2PM provides a scalable generation of realistic adversarial examples, which can be advantageous for both adversarial training and attacks.
- Adapting Meeting Tools to Agent DecisionPublication . Barreto, João; Praça, Isabel; Pinto, Tiago; Sousa, Tiago; Vale, ZitaElectricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.
- Adaptive learning in agents behaviour: A framework for electricity markets simulationPublication . Pinto, Tiago; Vale, Zita; Sousa, Tiago; Praça, Isabel; Santos, Gabriel; Morais, HugoElectricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.
- Adaptive Learning in Games: Defining Profiles of Competitor PlayersPublication . Pinto, Tiago; Vale, ZitaArtificial 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-paper-scissors 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.
- Adaptive learning in multiagent systems for automated energy contacts negotiationPublication . Pinto, Tiago; Vale, ZitaThis paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations.
- Adaptive learning in multiagent systems: a forecasting methodology based on error analysisPublication . Sousa, Tiago; Pinto, Tiago; Vale, Zita; Praça, Isabel; Morais, H.Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.