M. Dolores Jiménez-López

Computing Dialogues with Membranes

Human-computer interfaces require models of dialogue structure that capture the variability and unpredictability within dialogue. In this paper we propose to use a computing paradigm -P systems- in order to define such a dialogue model. We introduce Dialogue P Systems (shortly, DPS) as a biological computing model that computes pragmatic minimal units -speech acts- for constructing dialogues. We claim that DpS provide a simple model where the passage from the real dialogue to the P systems model can be achieved in a highly formalized way.

A grammatical framework for modelling multi-agent dialogues

In this paper we present a variant of grammar system from Formal Language theory, that seems to be appropriate for modelling dialogues in multi-agent systems. Previous attempts of simulating conversations in grammar systems used rewriting rules, which remained fix and unchangeable during the whole dialogue. The novelty of this approach is that taking inspiration from a Multi Agent Protocol language we define a grammatical system in which agents(grammars) behaviours are given by string process descriptions or protocols. Strings can be modified during running time, allowing to dynamically alter agent behaviour according to the environmental changes. This provides agents with a more flexible and adaptative reaction to unpredictable and changeable conversational space.

Womb Grammars as a Bio-inspired Model for Grammar Induction

Womb Grammars are a recently developed constraint-based framework designed for inducing the syntax of a human language from that of another, described in terms of properties [8]. They have been named by analogy to the generative power of human wombs, capable of engendering any race given appropriate input. In this article we integrate various facets of Womb Grammars from this bio-inspired viewpoint, and we extract from the various applications in which they have been used, the set of features and advantages that make them a unique tool among contemporary language processors. We also discuss extensions and limitations.

On the Syntax-Semantics Interface of Argument Marking Prepositional Phrases

The paper investigates a class of argument marking prepositional phrases that have two roles when they occur in some verb phrases. Primarily, such prepositional phrases provide the head verb with a noun phrase as one of its dependents, casting syntax-semantics correlations in the internal argument structure of the verb phrase. Additionally, they can carry substantial semantic information. The paper contributes to methods of language processing by integrated algorithmic syntax-semantics interface. The work is part of development of a new type-theoretic approach to the concepts of algorithm and algorithmic syntax-semantics interfaces.

Biocomputing: an insight from linguistics

This paper is placed in a formal framework in which the interdisciplinary study of natural language is conducted by integrating linguistics, computer science and biology. It provides an overview of the field of research, conveying the main biological ideas that have influenced research in linguistics. Our work highlights the main methods of molecular computing that have been applied to the processing and study of the structure of natural language: DNA computing, membrane computing and networks of evolutionary processors. Moreover, some new challenges and lines of research for the future are pointed out, that can provide important improvements in the understanding of natural language as a structure and a human capacity.

Agent-environment interaction in a multi-agent system: a formal model

In this paper, we introduce a formal-language model for explicitly formalizing agent-environment interaction in a multi-agent systems (MAS) framework: Conversational Grammar Systems(CGS). The main goal of the model is to provide a formal framework for defining how agents interact with environments in MAS. CGS offer a model with a high degree of flexibility, what means that they are able to accept new concepts and modify rules, protocols and settings during the computation. Evolution and action are involved in a consistent way in environment, while interaction of agents with the medium is constant. Based on a consolidated and active branch in the field of formal language theory, CGS are a highly formalized framework based in the postulates of artificial life that seems to be quite easy to implement, due to the simplicity of the formalism and the computational background of the theory they use.

Learning, Agents, and Formal Languages: Linguistic Applications of Interdisciplinary Fields

This paper focuses on three areas: machine learning, agent technologies and formal language theory. Our goal is to show how the interrelation among agents, learning and formal languages can contribute to the solution of a challenging problem: the explanation of how natural language is acquired and processed. Linguistic contributions of the intersection between machine learning and formal language theory –through the field of grammatical inference– are reviewed. Agent-based formal language models as colonies, grammar systems and eco-grammar systems have been applied to different natural language issues. We review the most relevant applications of these models.

An Overview of the Interrelation Among Agent Systems, Learning Models and Formal Languages

Considering the important role of interdiciplinarity in current research, this article provides an overview of the interchange of methods among three different areas: agent technologies, learning models and formal languages. The ability to learn is one of the most fundamental attributes of the intelligent behaviour. Therefore, any progress in the theory and computer modelling of learning processes is of great significance to fields concerning with understanding intelligence, and this includes, of course, artificial intelligence and intelligent agent technology. Agent technologies can offer good solutions and alternative frameworks to classic models in the area of computing languages and this can benefit formal models of learning. Formal language theory –considered as the stem of theoretical computer science– provides mathematical tools for the description of linguistic phenomena. This theory is central to grammatical inference, a subfield of machine learning. The interest of the interrelation among these disciplines is based on the idea that the collaboration among researchers in these areas can clearly improve their respective fields. Our goal here is to present the state-of-the art of the relationship among these three areas and to emphasize the importance of this interdisciplinary research.

Generation of classification trees from variable weighted features

Trees are a useful framework for classifying entities whose attributes are, at least partially, related through a common ancestry, such as species of organisms, family members or languages. In some common applications, such as phylogenetic trees based on DNA sequences, relatedness can be inferred from the statistical analysis of unweighted attributes. The vast majority of mutations that survive across generations are evolutionarily neutral, which means that most genetic differences between species will have accumulated independently and randomly. In these cases, it is possible to calculate the tree from a precomputed matrix of distances. In other cases, such as with anatomical traits or languages, the assumption of random and independent differences does not hold, making it necessary to consider some traits to be more relevant than others for determining how related two entities are. In this paper, we present a constraint programming approach that can enforce consistency between bounds on the relative weight of each trait and tree topologies, so that the user can best determine which sets of traits to use and how the entities are likely to be related.

A Grammar-Based Multi-Agent System for Language Evolution

Considering the adequacy of agent systems for the simulation of language evolution, we introduce a formal-language-theoretic multi-agentmodel based on grammar systems that may account for language change: cultural grammar systems. The framework we propose is a variant of the so-called eco-grammar systems. We modify this formal model, by adding new elements and relationships, in order to obtain a new machinery to describe the dynamics of the evolution of language.