BigData Analysis and Data Mining

Biography

Biography: Jose Manuel Lopez-Guede

Abstract

One of the key activities of Data Mining is to discover and make clear hidden relations and working rules in complex systems. Robotics is a complex scope in which first prinicples approaches have been used to solve straight problems, but that approach is not enought to deal with complex problems, where more intelligence-based approaches are needed. Data Mining can be used for autonomous learning of control algorithms for Linked Multicomponent Robotic Systems (L-MCRS), because it is an open research field. Single Robot Hose Transport (SRHT) is a limit case of this kind of systems, when one robot moves the tip of a hose to a desired position, while the other hose extreme is attached to a source position. Reinforcement Learning (RL) algorithms have been applied to learn autonomously the robot control in SRHT, specifically Q-Learning and TRQ-Learning have been applied with success. However storing the state-action value functional information in tabular form produces large and intractable data structures, and using the Data Mining approach the problem can be addressed by discivering and learning the state-action values of Q-table by Extreme Learning Machines (ELM), obtaining a data reduction because the number of ELM parameters is much less than the Q-table's size. Moreover, ELM implements a continuous map which can produce compact representations of the Q-table, and generalizations to increased space resolution and unknown situations.