A Neuroevolutionary Approach to Emergent Task Decomposition
نویسندگان
چکیده
Abstract. A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectures are compared for a collective robotics (3 × 3 tiling pattern formation) task where emergent behaviours and effective task -decomposition techniques are necessary to solve the task. We show that bigger, more modular network architectures that exploit emergent task decomposition strategies can evolve faster and outperform comparably smaller non emergent neural networks for this task. Much like biological nervous systems, larger Emergent Task Decomposition Networks appear to evolve faster than comparable smaller networks. Unlike reinforcement learning techniques, only a global fitness function is specified, requiring limited supervision, and self-organized task decomposition is achieved through competition and specialization. The results are derived from computer simulations.
منابع مشابه
Application of a Neuroevolutionary Approach to Emergent Task Decomposition in Collective Robotics
Abstract. A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectures are compared for a collective robotics (3 × 3 tiling pattern formation) task where emergent behaviours and effective task -decomposition techniques are necessary to solve the task. We show that bigger, m...
متن کاملUsing promoters and functional introns in genetic algorithms for neuroevolutionary learning in non-stationary problems
This paper addresses the problem of adaptive learning in non-stationary problems through neuroevolution. It is a general problem that is very relevant in many tasks, for example, in the context of robot model learning from interaction with the world. Traditional learning algorithms fail in this task as they have mostly been designed for learning a single model in a static setting. Neuroevolutio...
متن کاملCombination of Empirical Mode Decomposition Components of HRV Signals for Discriminating Emotional States
Introduction Automatic human emotion recognition is one of the most interesting topics in the field of affective computing. However, development of a reliable approach with a reasonable recognition rate is a challenging task. The main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (HRV). In t...
متن کاملA Coarse-Coding Framework for a Gene-Regulatory-Based Artificial Neural Tissue
A developmental Artificial Neural Tissue (ANT) architecture inspired by the mammalian visual cortex is presented. It is shown that with the effective use of gene regulation that large phenotypes in the form of Artificial Neural Tissues do not necessarily pose an impediment to evolution. ANT includes a Gene Regulatory Network that controls cell growth/death and activation/inhibition of the tissu...
متن کاملA Benders� Decomposition Approach for Dynamic Cellular Manufacturing System in the Presence of Unreliable Machines
In order to implement the cellular manufacturing system in practice, some essential factors should be taken into account. In this paper, a new mathematical model for cellular manufacturing system considering different production factors including alternative process routings and machine reliability with stochastic arrival and service times in a dynamic environment is proposed. Also because of t...
متن کامل