Reactive with tags classifier system applied to real robot navigation

7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules

RTCS: a reactive with tags classifier system

In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.Publicad

Automatic symbolic modelling of co-evolutionarily learned robot skills

Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001Evolutionary based learning systems have proven to be very powerful techniques for solving a wide range of tasks, from prediction to optimization. However, in some cases the learned concepts are unreadable for humans. This prevents a deep semantic analysis of what has been really learned by those systems. We present in this paper an alternative to obtain symbolic models from subsymbolic learning. In the first stage, a subsymbolic learning system is applied to a given task. Then, a symbolic classifier is used for automatically generating the symbolic counterpart of the subsymbolic model. We have tested this approach to obtain a symbolic model of a neural network. The neural network defines a simple controller af an autonomous robot. a competitive coevolutive method has been applied in order to learn the right weights of the neural network. The results show that the obtained symbolic model is very accurate in the task of modelling the subsymbolic system, adding to this its readability characteristic

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

Machine Learning and Cognitive Robotics: Opportunities and Challenges

The chapter reviews recent developments in cognitive robotics, challenges and opportunities brought by new developments in machine learning (ML) and information communication technology (ICT), with a view to simulating research. To draw insights into the current trends and challenges, a review of algorithms and systems is undertaken. Furthermore, a case study involving human activity recognition, as well as face and emotion recognition, is also presented. Open research questions and future trends are then presented