Cooperating distributed grammar systems with random context grammars as components

In this paper, we discuss cooperating distributed grammar systems where components are (variants of) random context grammars. We give an overview of known results and open problems, and prove some further results

On the Degree of Team Cooperation in CD Grammar Systems.

In this paper, we introduce a dynamical complexity measure, namely the degree of team cooperation, in the aim of investigating "how much" the components of a grammar system cooperate when forming a team in the process of generating terminal words. We present several results which strongly suggest that this measure is trivial in the sense that the degree of team cooperation of any language is bounded by a constant. Finally, we prove that the degree of team cooperation of a given cooperating/distributed grammar system cannot be algorithmically computed and discuss a decision problem

Systems of Sequential Grammars Applied to Parsing

Tato práce zkoumá Gramatické systémy jako potenciálně silnější nástroj pro syntaktickou analýzu, nežli obyčejné gramatiky. Hlavním záměrem je aplikace teoretických modelů do praxe, vytvoření syntaktického analyzátoru. Jsou zavedeny nové metody zaměřené na determinizmus, a tím vyhnutí se zpětnému navracení při analýze. Základem analyzátoru je CD gramatický systém. Implementace využívá metodu prediktivní syntaktické analýzy, překlad řízený tabulkou a také rekurzi. Analyzátor je univerzální, použitelný pro jakékoliv LL-Gramatiky a jakékoliv gramatické systémy na nich založené.This thesis examines Grammar systems as the potentially more powerful tool for parsing as the simple grammars. The intention is to adapt theoretical models of grammar systems for parsing. New methods are introduced, with focus on determinism in order to prevent backtracking during parsing. The basis for the parser is a cooperating distributed grammar system. The implementation uses predictive, top-down parsing method, LL(1)Tables, and recursion as well. The parser is universal, usable for any LL-Grammar and for any grammar system based on them.

Learning Parallel Grammar Systems for a Human Activity Language

We have empirically discovered that the space of human actions has a linguistic structure. This is a sensory-motor space consisting of the evolution of the joint angles of the human body in movement. The space of human activity has its own phonemes, morphemes, and sentences. In kinetology, the phonology of human movement, we define atomic segments (kinetemes) that are used to compose human activity. In this paper, we present a morphological representation that explicitly contains the subset of actuators responsible for the activity, the synchronization rules modeling coordination among these actuators, and the motion pattern performed by each participating actuator. We model a human action with a novel formal grammar system, named Parallel Synchronous Grammar System (PSGS), adapted from Parallel Communicating Grammar Systems (PCGS). We propose a heuristic PArallel Learning (PAL) algorithm for the automatic inference of a PSGS. Our algorithm is used in the learning of human activity. Instead of a sequence of sentences, the input is a single string for each actuator in the body. The algorithm infers the components of the grammar system as a subset of actuators, a CFG grammar for the language of each component, and synchronization rules. Our framework is evaluated with synthetic data and real motion data from a large scale motion capture database containing around 200 different actions corresponding to verbs associated with voluntary observable movement. On synthetic data, our algorithm achieves 100% success rate with a noise level up to 7%