Reinforcement schemes represent the basis of the learning process for stochastic learning automata, generating their learning behavior. An automaton using a reinforcement scheme can decide the best action, based on past actions and environment responses. The aim of this paper is to introduce a new reinforcement scheme for stochastic learning automata. We test our schema and compare with other n...

in electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. in order to attain such resources they need to compete with each other. in multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. in recent ...

one of the main challenges in wireless sensor network is energy problem and life cycle of nodes in networks. several methods can be used for increasing life cycle of nodes. one of these methods is load balancing in nodes while transmitting data from source to destination. directed diffusion algorithm is one of declared methods in wireless sensor networks which is data-oriented algorithm. direct...

The learning automata operate in unknown random environments and progressively improve their performance via a learning process. The learning automata are very useful for optimization of multi-modal functions when the function is unknown and only noise-corrupted evaluations are available. In this paper we propose a new hybrid algorithm for noisy optimization. This model is obtained by combining...

In this article we discuss (i) a model suitable for describing a distributed real-time system and (ii) a notion of implementation for such systems on a uniprocessor system. The first point is addressed by amalgamating finite state automata with dense time (or Alur-Dill automata) and asynchronous distributed (or Zielonka) automata over a distributed alphabet. The second point is addressed by def...

Combining ideas from distributed algorithms and alternating automata, we introduce a new class of finite graph automata that recognize precisely the languages of finite graphs definable in monadic second-order logic. By restricting transitions to be nondeterministic or deterministic, we also obtain two strictly weaker variants of our automata for which the emptiness problem is decidable. As an ...

We present an active learning algorithm named NRTALearning for nondeterministic real-time automata (NRTAs). Real-time (RTAs) are a subclass of timed with only one clock which resets at each transition. First, we prove the corresponding Myhill-Nerode theorem languages. Then show that there exists unique minimal deterministic automaton (DRTA) recognizing given language, but same does not hold NRT...