This paper presents new classes of tree automata combining automata with equality test and automata modulo equational theories. We believe that this class has a good potential for application in e.g. software verification. These tree automata are obtained by extending the standard Horn clause representations with equational conditions and rewrite systems. We show in particular that a generalize...

In this paper, we first introduce a new lower bound technique for the state complexity of transformations of automata. Namely we suggest considering the class of full automata in lower bound analysis. Then we apply such technique to the complementation of nondeterministic ωautomata and obtain several lower bound results. Particularly, we prove anΩ((0.76n)) lower bound for Büchi complementation,...

We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe state-merging algorithm for the problem of learning deterministic finite automata. In addition to sta...

We introduce a slightly modiied version of a standard learning automaton and show that this`responsive' learning automata eliminates dominated strategies when playing against an unknown environment. This new automaton has strong convergence properties that are easily analyzed, allowing us to compute explicit bounds for convergence rates. Groups of such automata, interacting via a general game, ...

With the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. One of the major problems in text classification relates to the high dimensional feature spaces. Therefore, the main goal of text classification is to reduce the dimensionality of features space. There are many feature selection methods. However...

Abstract In this paper, we consider a model of generalized timed automata (GTA) with two kinds clocks, history and future , that can express many features succinctly, including automata, event-clock without diagonal constraints, timers. Our main contribution is new simulation-based zone algorithm for checking reachability in unified model. While such algorithms are known to exist have recently ...

Learning automata act in a stochastic environment and are able to update their action probabilities considering the inputs from their environment, so optimizing their functionality as a result. In this paper, the goal is to investigate and evaluate the application of learning automata to cooperation in multiagent systems, using soccer simulation server as a test bed. We have also evaluated our ...

Active learning of register automata infers extended finite state machines (EFSMs) with registers for storing values from a possibly infinite domain, and transition guards that compare data parameters to registers. In this paper, we present RALib, an extension to the LearnLib framework for automata learning. RALib provides an extensible implementation of active learning of register automata, to...

Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that a set of decentralized, independent learning automata is able to control a finite Markov Chain with unknown transition probabilities and rewards. We extend this result to the framework of Multi-Agent MDP’s, a straigh...