Performance appraisal of Learning Automata in Networks Rohit Kumar Das Department of IT Assam University, Silchar Assam, India myown12@gmail.com Banani Das Department of IT Assam University, Silchar Assam, India banani.das.bd@gmail.com Sudipta Roy Department of IT Assam University, Silchar Assam, India sudipta.it@gmail.com Abstract—In stochastic environment, uncertainties are of the higher order to predict the future events. Learning automata (LA) can be used in such a system where prior information is available. These automata are capable of performing better and better with their updating potential of input to the system with respect to the environment. Learning automata can be used in different networks for the proper management of the network performances. A survey for learning automata with networks as an environment has been reported in this paper. This paper presents a detailed investigation on the behavior of learning automata, various networks in which learning system can operates. Keywords-stochastic environment; probability; learning automata (LA); wireless networks I. INTRODUCTION Stochastic environment is a part of a probability theory where the environments are fully non-deterministic and therefore this lead to the case of probability. In other words, the system possesses completely unorganized events in a random fashion hence are unpredictable or the degree of uncertainty are of higher order like all the natural events. Stochastic environment can be seen in business area where the internal environment is affected by random events in the external environment. If the system changes randomly then the present assumptions may result insufficient for the control system to be successfully operate which may lead to further gathering of knowledge of the environment upon which it is operating. Therefore learning during executing becomes an important part for this type of systems where additional information is required at any part of the process. Stochastic automaton acting in this manner to improve its performance is referred to as a learning automaton in this paper. It can update its action probabilities which results in reduction in the number of states in comparison with deterministic automata. Learning can be stated as reinforcing the existing knowledge and leads in change of behavior. Learning cannot be done in just a single step rather it’s a process of collecting procedural knowledge. A learning automaton is a part of intelligent system which has the capability to adapt the changes in environments with unknown characteristics by a learning process [1]. The rest of the paper is organized as: Section II provides a brief earlier works in LA. Idea and definition for learning automata is being given in Section III. The core working with reinforcement scheme is elaborated in Section IV. Section V provides the information of how learning automata are used in different stochastic environment. Function of automaton in various layer of network is provided in Section VI. Finally, Section VII concludes the paper. II. EARLIER WORKS IN LA The foremost learning automata models were developed in mathematical psychology. Bush and Mosteller [2] documented a book which contains the earlier work in this area. A detail work has been presented by the authors of [3] in this field. The perception of using deterministic automata operating in random environments as models of learning is being given by Tsetlin [4]. Varshavski and Vorontsova [5] described the use of stochastic automata with updating of action probabilities which results in reduction in the number of states in comparison with deterministic automata. In their paper they have given very innovative ideas which are used in many research works. In [6] the author attempted to illustrate the updating schemes which is portray through inverse optimization problem. In [7] McLaren suggested the concept of a growing automaton by exploring the properties of linear updating schemes. Learning automata to real life problems include control of fascination columns, bioreactors, management of manufacturing plants, pattern recognition, path planning and action selection for autonomous mobile robots, graph partitioning, and active vehicle suspension, path planning for manipulators and distributed fuzzy logic processor training. III. LEARNING AUTOMATA Learning Automata is a part of theory of intelligent system where the learning system interacts with the stochastic environment by applying its input and collects the result back to the system all the way through the feedback mechanism which is later updated and is stored in the updating matrix of the automata. In other words, learning automata is a finite state mechanism which interacts with environment and gets the optimal action back. 2014 Fourth International Conference on Communication Systems and Network Technologies 978-1-4799-3070-8/14 $31.00 © 2014 IEEE DOI 10.1109/CSNT.2014.227 1110 2014 Fourth International Conference on Communication Systems and Network Technologies 978-1-4799-3070-8/14 $31.00 © 2014 IEEE DOI 10.1109/CSNT.2014.227 1110