Fernando Santos Osório

INSS: A hybrid system for constructive machine learning

Abstract In this paper we present the INSS system, a new hybrid approach based upon the principles of KBANN networks. It represents an important improvement in comparison with its predecessor because the learning and the knowledge extraction process are faster and are accomplished in an incremental way. INSS offers a new approach applicable to constructive machine learning with high-performance tools, even in the presence of incomplete or erroneous data.

Integrating Intelligent Agents, User Models, and Automatic Content Categorization in a Virtual Environment

This paper presents an approach that aims to integrate intelligent agents, user models and automatic content categorization in a virtual environment. In this environment, called AdapTIVE (Adaptive Three-dimensional Intelligent and Virtual Environment), an intelligent virtual agent assists users during navigation and retrieval of relevant information. The users’ interests and preferences, represented in a user model, are used in the adaptation of environment structure. An automatic content categorization process, that applies machine-learning techniques, is used in the spatial organization of the contents in the environment. This is a promising approach for new and advanced forms of education, entertainment and e-commerce. In order to validate our approach, a case study of a distance-learning environment, used to make educational content available, is presented.

An intelligent and adaptive virtual environment and its application in distance learning

This paper presents an intelligent and adaptive virtual environment, which has its structure and presentation customized according to users interests and preferences (represented in a user model) and in accordance with insertion and removal of contents in this environment. An automatic content categorization process is applied to create content models, used in the spatial organization of the contents in the environment. An intelligent agent assists users during navigation in the environment and retrieval of relevant information. In order to validate our proposal, a prototype of a distance learning environment, used to make educational content available, was developed.

AdapTIVE: an intelligent virtual environment and its application in e-commerce

This work presents an intelligent virtual environment, called AdapTIVE (adaptive three-dimensional intelligent and virtual environment), which has its structure and presentation customized according to users interests and preferences (represented in a user model) and in accordance with insertion and removal of contents in this environment. An automatic content categorization process is applied to create content models, used in the spatial organization of the contents in the environment. An intelligent agent assists users during navigation in the environment and retrieval of relevant information. This is a promising approach for new and advanced forms of education, entertainment and e-commerce. In order to validate our approach, a case study of an e-commerce environment is presented.

Gait control generation for physically based simulated robots using genetic algorithms

This paper describes our studies in the legged robots research area and the development of the LegGen System, that is used to automatically create and control stable gaits for legged robots into a physically based simulation environment. The parameters used to control the robot are optimized using Genetic Algorithms (GA). Comparisons between different fitness functions were accomplished, indicating how to compose a better multi-criterion fitness function to be used in the gait control of the legged robots. The best gait control solution and the best robot model were selected in order to help us to build a real robot in the future. The results also showed that it is possible to generate stable gaits using GA in an efficient manner.

Neural Networks Applied to Gait Control of Physically Based Simulated Robots

This paper describes our experiments with autonomous robots, in which we use neural networks to generate and control stable gaits of simulated legged robots into a physically based simulation environment. In our approach, the gait is accomplished using an Elman network trained using a gradient descend method, more specifically, the RPROP algorithm, a improvement of the traditional Back-propagation. The model validation was performed by several experiments realized with a simulated four legged robot using the ODE physical simulation engine. The results showed that it is possible to generate stable gaits using neural networks in an efficient manner.

Applying Genetic Algorithms to Control Gait of Simulated Robots

This paper describes the LegGen simulator, used to automatically create and control stable gaits for legged robots into a physically based simulation environment. In our approach, the gait is defined using two different methods: a finite state machine based on robots leg joint angles sequences; and a recurrent neural network. The parameters for both methods are optimized using genetic algorithms. The model validation was performed by several experiments realized with a robot simulated using the ODE physical simulation engine. The results showed that it is possible to generate stable gaits using genetic algorithms in an efficient manner, using these two different methods.

Assistance controller for driving backwards and parking an articulated vehicle

Driving backwards and parking articulated vehicles represent a hard procedure also for skilled drivers. If a vehicle is semi-automated in a way that a computer can command the steering wheel, a driver assistance system may help the conductor to perform such maneuvers easily. This work presents a solution for this problem. A self constructed prototype was developed to analyze the effectiveness of the proposed control strategies that include a stabilizing controller for the joint angle and a path tracking controller. The results show that the stabilizing controller permits an untrained driver to steer the vehicle backwards by setting up the joint angle reference signal with an external human machine interface while the path tracking controller allows the vehicle to follow a predetermined route autonomously.

Adaptive filters for image processing based on artificial neural networks

The paper presents our research in image processing (filters used for edge detection, color transformation, and distortion correction) using artificial neural networks. We have developed a tool, Neuron Color, in order to carry out these experiments and improving our adaptive methods.

Morphology and Gait Control Evolution of Legged Robots

This paper describes our research and experiments with autonomous robots, in which were used genetic algorithms to evolve stable gaits of simulated legged robots in a physically based simulation environment. In our approach, the gait is defined using a finite state machine based on the joint angles of the robot legs, and the parameters are optimized using genetic algorithms. The proposed model also allows the evolution of the robot body morphology. The model validation was performed by several experiments and a valid statistical analysis, and the results show that it is possible to generate fast and stable gaits using genetic algorithms in an efficient manner.