Abstract—The solving strategy of artificial intelligence (AI) is adopted with bottom-up design to solve its hard problems. To tackle end-to-end AI-hard problems, a highly self-adaptive control system-on-chip has been developed to self-learn its internal and external resources with the aid of sets of sensors and actuators. Inspired by biological cell learning theory, different approaches of modelling techniques have been derived together with machine learning methods to the embedded control systems so as to perform different tasks. Some experimental results have shown the developments. I. INTRODUCTION ith its strong inter-disciplinary characteristics, machine learning is the study of computer algorithms that improve automatically through experience [9]. Many different machine learning (ML) techniques have been developed recently. Decision trees are used in knowledge engineering and data mining applications, expert systems can help tackle specific problems that require comprehensive knowledge. Fuzzy sets can calibrate vagueness, as required for natural human language processing. Artificial neural networks (ANNs) are inspired from interconnections of brains cells. They are ‘processing memories’ where the knowledge is distributed over tiny units that operate in parallel. Algorithms inspired from genetic processes can be applied to solve any problem in which the solutions can be encoded. GA (Genetic Algorithm) approaches can be taken when a “good enough” solution is required quickly, but without the need to understand the rationale of this particular solution(s). Indeed, to guide the evolution of a GA, we preliminary need to define the fitness function or landscape function that corresponds to the given problem we wish to tackle. The main goal of such algorithms is to increase the machine knowledge, while other goals are to allow the machine to achieve tasks based on the recorded knowledge (i.e. the experience) and on the currently perceived Y. Zhou is with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and also with Shenzhen Key Laboratory of Electric Vehicle Powertrain Platform and Safety Technology, Shenzhen, China, 518055 (phone: 86-755-86392152; fax: 86-755-86392194; e-mail: ym.zhou@ siat.ac.cn). L.Krundel, D. Mulvaney and V. Chouliaras are with Department of Electronic and Electrical Engineering, Loughborough University, Loughborough, UK, LE11 3TU (e-mail: l.a.krundel, d.j.mulvaney, v.a.chouliaras@lboro.ac.uk). G. Xu is with Shenzhen Insitutes of Advanced Technology, Chinese Academy of Sciences, and also with The Chinese University of Hong Kong, Hong Kong (email: gq.xu@siat.ac.cn). G. Fu is with School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China (e-mail: gqfu@bjtu.edu.cn). surrounding information, and perhaps ultimately to allow the machine to think [4,5] in order to permit the solution of complex problems that would not normally be possible to tackle. Conventional Cellular Automata (CA) is a natural way of studying the evolution of large physical systems and constitute a general paradigm for parallel computation [14]. They are clocked: whole 2D CA landscape updated at each clock tick. They can be of nth-order if they are made of n interacting 2D cell layers. Each cell can usually have two possible states: e.g., alive or dead. They are governed by local (action-at-a-distance forbidden) and uniform (single rules’ recipe) system’s rules. Interestingly, one can define a meta-rule, which consists of several sets of rules and a strategy to dictate which rule’s set applies and when in the CA progress. This can bring up sophisticated behaviours. Cellular automata have properties that make them suitable for bottom-up design [14], including the generation of emergent NN structures. In highly distributed, massively parallel reconfigurable machines (such as CAs), spontaneous organization and emergence of intelligent behaviours can occur [11]. As systems grow in complexity, there is a huge need for features such as self-management of system resources. Unfortunately, conventional computing systems have to incorporate this feature as an additional software application that is cumbersome or impossible to maintain. However, redundant cellular systems are inherently scalable and self-management is straight forward to design and implement at elaboration time, and to maintain at simulation/run time. CAs also lend themselves to self-reorganization and can be easily restructured at run-time. Finally, in CAs the processing units are distributed, which ensures better fault-tolerance and handling of indeterminism. This paper discusses the CA learning rules and preliminary results. The reminder of the paper is organized as follows. In section II, background of the learning methods in neural networks is introduced. Methods of self-configuration are discussed in section III. The theory and learning rules of cellular automata technique and related proposed projects based on CA rules are explained in section IV. Preliminary results are given in section V. II. BACKGROUND A. Review Stage Autonomous learning design in System-on-chip Yimin Zhou, Member, IEEE , Ludovic Krundel, Member, IEEE , David Mulvaney, Vassilios Chouliaras, Guoqing Xu, Member, IEEE , Guoqiang Fu W 978-1-4799-2744-9/13/$31.00 ©2013 IEEE Proceeding of the IEEE International Conference on Robotics and Biomimetics (ROBIO) Shenzhen, China, December 2013 1054