In this paper, we present a theoretical approach for the problem of learning multiplicity tree automata. These automata allows one to define functions which compute a number for each tree. They can be seen as a strict generalization of stochastic tree automata since they allow to define functions over any field K. A multiplicity automaton admits a support which is a non deterministic automaton....

In this paper, a rate controller for a H.261 based video encoder is proposed. The rate controller adaptively chooses the optimal channel matched quantizer using a stochastic learning automaton. The automaton learns the channel characteristics based on a one bit feedback from the decoder. The rate control algorithm is shown to converge to the optimal choice of the quantizer very quickly for vari...

In this paper, we outline the design of a nondeterministic finite state automaton (NFSA) for natural language morphology, and compare it to previous work in unsupervised learning of morphology. In Section 2, we describe the nature of an MDL-based system for unsupervised learning of morphology, using the signature-based model of Goldsmith 2001 as an example, and we describe some drawbacks of the...

We present a supervised, interactive learning technique that infers control structures of computer programs from user-demonstrated traces. A two-stage process is applied: rst, a minimal deterministic nite automaton (DFA) M labeled by the instructions of the program is learned from a set of example traces and membership queries to the user. It accepts all pre xes of traces of the target program....

We consider the problem of a robot manipulator operating in a noisy workspace. The robot is assigned the task of moving from Pi to Pf. Since Pi is its initial position, this position can be known fairly accurately. However, since Pf is usually obtained as a result of a sensing operation, possibly vision sensing, we assume that Pf i s noisy. We propose a solution to achieve the motion which invo...

This paper proposes two novel beam-forming methodologies, which employ passive RF devices to enhance received signal through a passive, scattering based beam forming. Currently available backscattering systems, e.g. the radio frequency identification (RFID), suffer from a poor communication in an environment with multiple passive devices. The intermediate antennas create severe scattering envir...

The central aim of this paper and its projected successors is to prove in detail that stimulus-response theory, or at least a mathematically precise version, can give an account of the learning of many phrase-structure grammars. Section 2 is concerned with standard notions of finite and probabilistic automata. An automaton is defined as a device with a finite number of internal states. Section ...

Moore’s seminal paper [9] can be taken as the starting point of Algorithmic Learning Theory. Moore studied the problem of unraveling the inner structure of a (minimum state) deterministic finite automaton (DFA) from its input-output behaviour. In this note we pursue Moore’s line of research, studying conditions under which it is possible to compute a grammar and/or an automaton for a given lang...

We present a novel canonical automaton model, based on register automata, that can be used to specify protocol or program behavior. Register automata have a finite control structure and a finite number of registers (variables), and process sequences of terms that carry data values from an infinite domain. We consider register automata that compare data values for equality. A major contribution ...

In this paper, we propose a new continuous action learning automaton and an adaptive and autonomous call admission algorithm, which uses the proposed learning automaton. The proposed algorithm minimizes blocking probability of new calls subject to constraint on dropping probability of handoff calls. The simulation results show that the performance of this algorithm is close to the performance o...