Wireless link scheduling is one of the major challenging issues in multi hop wireless networks when they need to be designed in distributed fashion. In this work we improve the general randomized scheduling method by using learning automata based framework that allows throughput optimal scheduling algorithms could be developed in distributed fashion. We propose a distributed scheduling algorith...

This paper introduces a novel payoff-based learning scheme for distributed optimization in repeatedly-played strategic-form games. Standard reinforcement-based learning schemes exhibit several limitations with respect to their asymptotic stability. For example, in two-player coordination games, payoff-dominant (or efficient) Nash equilibria may not be stochastically stable. In this work, we pre...

Distributed Learning Automata is automata based modelling approach for solving stochastic shortest path problems. The DLA can be applied to road networks to find shortest path that provides a spatial approach to bottom-up modelling of complex geographic systems that are comprised of infrastructure and human objects. Route finding is a popular Geographical Information System (GIS) application un...

ad hoc mobile networks have dynamic topology with no central management. because of the high mobility of nodes, the network topology may change constantly, so creating a routing with high reliability is one of the major challenges of these networks .in the proposed framework first, by finding directions to the destination and calculating the value of the rout the combination of this value with ...

multi agent markov decision processes (mmdps), as the generalization of markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for multi agent reinforcement learning. in this paper, a generalized learning automata based algorithm for finding optimal policies in mmdp is proposed. in the proposed algorithm, mmdp ...

In this report, a novel approach to intelligence and learning is introduced; this approach is based upon what we called perception logic. What we call ‘perception automata’ is introduced in which learning is accomplished at different perception resolution. Learning in this automata is not heuristic, rather it guarantees the convergence of the approximated function to whatever precision required...

optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, beca...

coverage improvement is one of the main problems in wireless sensor networks. given a finite number of sensors, improvement of the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. for achieving good coverage, the sensors should be placed in adequate places. this paper uses the genetic and learning automata as intelligent methods for...

Cellular learning automata (CLA) is a distributed computational model which was introduced in the last decade. This model combines the computational power of the cellular automata with the learning power of the learning automata. Cellular learning automata is composed from a lattice of cells working together to accomplish their computational task; in which each cell is equipped with some learni...