A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding “macro-actions”, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task

Navigation control of an automated mobile robot robot using neural network technique

Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue is the provision of sensors to study the earth. As neural networks (NNs) are described by adaptability and a fitness for managing non-linear problems, they are conceived to be useful when utilized on navigation robots. In this exploration our computerized reasoning framework is focused around neural network model for control of an Automated motion robot in eccentric and unsystematic nature. Hence the back propagation algorithm has been utilized for controlling the direction of the mobile robot when it experiences by an obstacle in the left, right and front directions. The recreation of the robot under different deterrent conditions is carried out utilizing Arduino which utilizes C programs for usage

Neuroevolution: from architectures to learning

Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can provide an automatic solution to these problems. New insights in both neuroscience and evolutionary biology have led to the development of increasingly powerful neuroevolution techniques over the last decade. This paper gives an overview of the most prominent methods for evolving ANNs with a special focus on recent advances in the synthesis of learning architecture

CODA Algorithm: An Immune Algorithm for Reinforcement Learning Tasks

This document presents the design of an algorithm that takes on its basis: reinforcement learning, learning from demonstration and most importantly Artificial Immune Systems. The main advantage of this algorithm named CODA (Cognition from Data). Is; it can learn from limited data samples- that is given a single example and the algorithm will create its own knowledge. The algorithm imitates from the Natural Immune System the clonal procedure for obtaining a repertoire of antibodies from a single antigen. It also uses the self-organised memory in order to reduce searching time in the whole action-state space by searching in specific clusters. CODA algorithm is presented and explained in detail in order to understand how these three principles are used. The algorithm is explained with pseudocode, flowcharts and block diagrams. The clonal/mutation results are presented with a simple example. It can be seen graphically how new data that has a completely new probability distribution. Finally, the first application where CODA is used, a humanoid hand is presented. In this application the algorithm created affordable grasping postures from limited examples, creates its own knowledge and stores data in memory data in memory in order to recognise whether it has been on a similar situation