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.

On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks.

Towards the automatic generation of card games through Grammar-Guided Genetic Programming

We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representa- tive examples of generated games are analysed. We observed that these games are reasonably balanced and have skill ele- ments, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality games

A card Game Description Language

We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for com- putationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar G cardgame capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texashold?em , Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammar- guided genetic program.

Large-scale assessment of language proficiency: theoretical and pedagogical reflections on the use of multiple-choice tests

The new requirement placed on students in tertiary settings in Spain to demonstrate a B1 or a B2 proficiency level of English, in accordance with the Common European Framework of Reference for Languages (CEFRL), has led most Spanish universities to develop a program of certification or accreditation of the required level. The first part of this paper aims to provide a rationale for the type of test that has been developed at the Universidad Politécnica de Madrid for the accreditation of a B2 level, a multiple choice version, and to describe how it was constructed and validated. Then, in the second part of the paper, the results from its application to 924 students enrolled in different degree courses at a variety of schools and faculties at the university are analyzed based on a final test version item analysis. To conclude, some theoretical as well as practical conclusions about testing grammar that affect the teaching and learning process are drawn. RESUMEN. Las nuevas exigencias sobre niveles de competencia B1 y B2 en inglés según el Marco Común Europeo de Referencia para las Lenguas (MCERL) que se imponen sobre los estudiantes de grado y posgrado han llevado a la mayoría de las universidades españolas a desarrollar programas de acreditación o de certificación de estos niveles. La primera parte de este trabajo trata sobre las razones que fundamentan la elección de un tipo concreto de examen para la acreditación del nivel B2 de lengua inglesa en la Universidad Politécnica de Madrid. Se trata de un test de opción múltiple y en esta parte del trabajo se describe cómo fue diseñado y validado. En la segunda parte, se analizan los resultados de la aplicación del test a gran escala a un total de 924 estudiantes matriculados en varias escuelas y Facultades de la Universidad. Para terminar, se apuntan una serie de conclusiones teóricas y prácticas sobre la evaluación de la gramática y de qué modo influye en los procesos de enseñanza y aprendizaje.

Autonomous Acquisition of Natural Language

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora.