Symbolic automata allow transitions to carry predicates over rich alphabet theories, such as linear arithmetic, and therefore extend classic automata to operate over infinite alphabets, such as the set of rational numbers. In this paper, we study the foundational problem of learning symbolic automata. We first present Λ∗, a symbolic automata extension of Angluin’s L∗ algorithm for learning regu...

Weighted automata (WFAs) provide a general framework for the representation of functions mapping strings to real numbers. They include as special instances deterministic finite automata (DFAs), hidden Markov models (HMMs), and predictive states representations (PSRs). In recent years, there has been a renewed interest in weighted automata in machine learning due to the development of efficient ...

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...

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...