AIML

"Este trabajo de investigación se origina a partir de un proyecto realizado en la institución relacionado con un agente conversacional corpóreo, ya que actualmente el uso de los Agentes Conversacionales Animados ha ido creciendo. Estos suelen ser una herramienta utilizada en ciertas funciones en las cuales pueden servir como guías, maestros, ayudantes, etc.

La problemática que abordamos en éste documento, es la implementación de un agente conversacional capaz de expresar diálogos creíbles en lenguaje natural, expresando en sus diálogos rasgos de personalidad, emociones y su intensidad. Para la generación de diálogos creíbles, proponemos utilizar la extensión que se le hizo al lenguaje AIML (Artificial Intelligence Markup Language), utilizando la nueva estructura para los archivos de la base de conocimientos basados en AIML, mediante diálogos creíbles, es capaz de crear escenarios más reales para que el aspirante disperse sus dudas con relación a la
Maestría en Ciencias en Ciencias de la Computación, manteniendo una conversación simulada por el agente conversacional. Para esto se presentó una metodología para organizar y diseñar la estructura de los diálogos de un Agente Conversacional Animado capaz de expresar emociones en su diálogo. Resultando de éste trabajo un Asistente Virtual con Diálogo Emocional que proporciona información sobre la Maestría en Ciencias en Ciencias de la Computación del ITCM."

En este documento se presentan los diferentes tipos de agentes conversacionales como lo son: los Agentes Conversacionales Animados, Humanos Virtuales Creíbles, Asistentes Virtuales,
así como sus características, áreas de aplicación, y roles. Existen diversas empresas que se dedican a crear dichos agentes, los cuales deben contar con una base de conocimientos en un tema en específico. Para lograr que un agente conteste coherentemente se puede llevar a cabo un análisis de la comunicación, el cual es importante en la interacción cara-a-cara, con el fin de analizar la conversación y el discurso; los que tienen como base los actos del habla, para este caso en particular: los actos locutivos. Esto con el objetivo de llegar a crear un Sistema de Diálogo que tenga el rol de un Asistente Virtual.

"Este trabajo de investigación se origina a partir de un proyecto de la Universidad de Barcelona para desarrollar habilidades en el diagnóstico del Trastorno de Ansiedad Generalizada a estudiantes de psicología y psiquiatría utilizando un chatbot, el cual es un programa que intenta simular una conversación humana con una persona [Laven, 1996].

La problemática que abordamos en éste documento, es convertir un chatbot en un agente conversacional capaz de generar diálogos creíbles y dinámicos en lenguaje natural, expresando en sus diálogos rasgos de personalidad, emociones y su intensidad. Para la generación de diálogos creíbles, proponemos realizar una extensión al lenguaje AIML (Artificial Intelligence Markup Language), proponiendo una nueva estructura para los
archivos de conocimiento basados en AIML, la cual será la estructura de la base de conocimientos de nuestro agente conversacional.

El agente conversacional, mediante diálogos creíbles, es capaz de crear escenarios más reales para que el estudiante diagnostique de forma contundente el padecimiento simulado por el agente conversacional. Para determinar si los usuarios perciben que a través del diálogo textual se expresa una emoción y para determinar si una imagen refuerza la emoción transmitida en los diálogos se aplicó una encuesta. Las emociones evaluadas fueron Miedo, Alegría, Tristeza, Enojo, Angustia, Placer, Sorpresa y Resignación. La encuesta se dividió en dos partes y muestra una pequeña conversación entre un usuario y el ECA, en la primera parte, se utilizaron sólo frases y en la segunda, éstas fueron reforzadas con imágenes del agente conversacional, acordes a la emoción a transmitir en el diálogo. Los resultados muestran que el porcentaje de identificación de la emoción y de su intensidad en las frases fueron del 57.50% y del 43.92% y que los porcentajes de identificación al reforzar las frases con imágenes fueron del 70.51% y del 55.35%, respectivamente. Estos resultados indican que utilizar imágenes reforzando a las frases contribuye en la identificación de las emociones y de sus intensidades."

We provide a strongly complete infinitary proof system for hybrid logic. This proof system can be extended with countably many sequents. Thus completeness proofs are provided for infinitary hybrid versions of non-compact logics like ancestral logic and Segerberg's modal logic with the bounded chain condition. This extends the completeness result for hybrid logics by Blackburn and Tzakova. Keywords: hybrid logic, strong completeness, non-compact logics, infinitary proof rules

This paper is based on a project at the University of Barcelona to develop the skills to diagnose the Generalized Anxiety Disorder (GAD) in students of psychology and psychiatry using a chatbot. The problem we address in this paper is to convert a chatbot in an emotional conversational agent capable of generating a believable and dynamic dialogue in natural language. For it, the dialogues convey traits of personality, emotions and its intensity. We propose to make an AIML language extension for the generation of believable dialogue, this extension will allow to create a more realistic scenario for the student to diagnose the condition simulated by the conversational agent. In order to measure the perception of the emotional state of the ECA expressed by the speech acts a survey was applied.

In this paper, an educational software used to enhance the skills of children in the area of math is presented. This software has a tutor who guides the child in different activities. The tutor exhibits personality traits and emotions in its dialogue to create a sense of immersion in the student and catch his attention towards the game. We adapted an Emotional Extension of the Artificial Intelligence Markup Language, structure here named as EE-AIML.

This paper is based on a project at the University of Barcelona to develop the skills to diagnose the Generalized Anxiety Disorder (GAD) in students of psychology and psychiatry using a chatbot. The problem we address in this paper is to convert a chatbot in an emotional conversational agent capable of generating a believable and dynamic dialogue in natural language. For it, the dialogues convey traits of personality, emotions and its intensity. We propose to make an AIML language extension for the generation of believable dialogue, this extension will allow to create a more realistic scenario for the student to diagnose the condition simulated by the conversational agent. In order to measure the perception of the emotional state of the ECA expressed by the speech acts a survey was applied.

Abstract.
This paper presents educational software based on a math tutor for elementary school children. This software teaches the basics operations of mathematics through a virtual tutor endowed with personality. The virtual tutor is a cognitive agent that integrates a behavioral model, verbal and nonverbal expressions. This architecture was based on the concepts of computing Clouding and RIA (Rich Internet Application) for easy access through Internet and mobile devices.

Reduction axioms for epistemic actions Barteld Kooi⁄ and Johan van Benthem† Abstract: Current dynamic epistemic logics often become cumbersome and opaque when common knowledge is added for groups of agents. We propose new versions that extend the underlying static ...

Sign language is the basic means of communication among hearing-impaired people. Systems that could act as interpreters between vocal and hearing-impaired people would facilitate the life of deaf and integrate them in the society. Such systems should perform bidirectional translation of sign language and spoken language. A lot of efforts invested on translating sign languages to spoken languages. However, it is just as important to translate a spoken language to a sign language. This would provide a two-way communication between hearing-impaired and vocal people. A reasonably priced portable computer would be sufficient to perform the two-way translation. This paper proposes a system that translates Arabic text to Arabic sign language. A word that corresponds to a sign from the Arabic sign language dictionary calls a pre-recorded video clip showing the sign played on the monitor of the portable computer. If the word does not have a corresponding sign in the dictionary, it is finger ...

In terms of validity in Kripke frames, a modal formula expresses a universal monadic second-order condition. Those modal formulae which are equivalent to first-order conditions are called elementary. Modal formulae which have a certain persistence property which implies their validity in all canonical frames of modal logics axiomatized with them, and therefore their completeness, are called canonical. This is a survey of a recent and ongoing study of the class of elementary and canonical modal formulae. We summarize main ideas and results, and outline further research perspectives.

Current dynamic epistemic logics often become cumbersome and opaque when common knowledge is added for groups of agents. We propose new versions that extend the underlying static epistemic languages in such a way that completeness proofs for the full dynamic systems can be obtained by perspicuous reduction axioms.

We provide a strongly complete infinitary proof system for hybrid logic. This proof system can be extended with countably many sequents. Thus completeness proofs are provided for infinitary hybrid versions of non-compact logics like ancestral logic and Segerberg's modal logic with the bounded chain condition. This extends the completeness result for hybrid logics by Blackburn and Tzakova. Keywords: hybrid logic, strong completeness, non-compact logics, infinitary proof rules

This paper is based on a project at the University of Barcelona to develop the skills to diagnose the Generalized Anxiety Disorder (GAD) in students of psychology and psychiatry using a chatbot. The problem we address in this paper is to convert a chatbot in an emotional conversational agent capable of generating a believable and dynamic dialogue in natural language. For it, the dialogues convey traits of personality, emotions and its intensity. We propose to make an AIML language extension for the generation of believable dialogue, this extension will allow to create a more realistic scenario for the student to diagnose the condition simulated by the conversational agent. In order to measure the perception of the emotional state of the ECA expressed by the speech acts a survey was applied.

This paper presents a Neural Network Classifier to be implemented in corner detection of chain code series. The classifier directly uses chain code which is derived using Freeman chain code as training, testing and validation set. The steps of developing Neural Network Classifier are included in this paper. Comparison results between Neural Network Classifier corner detection and other computational corner detection are presented to show the reliability of the proposed classifier. This paper ends with the discussions on the implementation of proposed neural network in corner detection of chain code series. Experimental results have shown that the proposed network has good robustness and detection performance. This makes this method a great choice for machine vision.

We consider multi-modal logics interpreted over edge-labelled graphs with a modality # , where # ϕ means 'ϕ is accessible by an edge with some label'. In a logic with finitely many edge labels, # is definable, but if the set of labels is infinite, it is an independent modality. We axiomatise multi-modal K, deterministic multi-modal K, and PDL with converse and a single nominal, extended with #. The latter gives an axiomatisation of the logic PDL path introduced in [3].

In this paper we identify modal logics of some bimodal Kripke frames corresponding to geometrical structures. Each of these frames is a set of 'geometrical' objects with some natural accessibility relation plus the universal relation. For these logics we present nite axiom systems and prove completeness. We also show that all these logics have the nite model property and are PSPACE-complete. To prove this, we show that under certain restrictions, adding the universal modality preserves 'good' properties of a monomodal logic.

In terms of validity in Kripke frames, a modal formula expresses a universal monadic second-order condition. Those modal formulae which are equivalent to first-order conditions are called elementary. Modal formulae which have a certain persistence property which implies their validity in all canonical frames of modal logics axiomatized with them, and therefore their completeness, are called canonical. This is a survey of a recent and ongoing study of the class of elementary and canonical modal formulae. We summarize main ideas and results, and outline further research perspectives.

In this paper we prove that Japaridze's Polymodal Logic is PSPACE-decidable. To show this, we describe a decision procedure for satisfiability on hereditarily ordered frames that can be applied to obtain upper complexity bounds for various modal logics.

We provide a detailed analysis of very weak fragments of modal logic. Our fragments lack connectives that introduce nondeterminism and they feature restrictions on the modal operators, which may lead to substantial reductions in complexity. Our main result is a general game-based characterization of the expressive power of our fragments over the class of finite structures.

Reduction axioms for epistemic actions Barteld Kooi⁄ and Johan van Benthem† Abstract: Current dynamic epistemic logics often become cumbersome and opaque when common knowledge is added for groups of agents. We propose new versions that extend the underlying static ...

Model combination in classification, density estimation and clustering and forecasting has become a promising research area where better results are obtained compared to using single models. Bayesian inference offers two approaches for combining models; Bayesian model averaging (BMA), and Supra-Bayesian classifier combination (SBCC). In this paper we briefly overview the BMA, show its drawbacks, and propose a novel idea of Bayesian data/model averaging. Then, the SBCC technique is explained, and its implementation used in this work is presented. A novel approach is proposed where input space is clustered before applying the SBCC. A 2-dimensional, 2-class, 4-experts problem is used to test the SBCC and SBCC-clustering algorithms, where better results are obtained over 300 runs for the data set, compared to an averaging technique, where the 4 experts’ performance is averaged.

We provide a detailed analysis of very weak fragments of modal logic. Our fragments lack connectives that introduce nondeterminism and they feature restrictions on the modal operators, which may lead to substantial reductions in complexity. Our main result is a general game-based characterization of the expressive power of our fragments over the class of finite structures.

In this paper we identify modal logics of some bimodal Kripke frames corresponding to geometrical structures. Each of these frames is a set of 'geometrical' objects with some natural accessibility relation plus the universal relation. For these logics we present finite axiom systems and prove completeness.

Data mining technique in the history of medical
data found with enormous investigations found that the
prediction of heart disease is very important in medical science.
In medical history it is observed that the unstructured data as
heterogeneous data and it is observed that the data formed with
different attributes should be analyzed to predict and provide
information for making diagnosis of a heart patient. Various
techniques in Data Mining have been applied to predict the heart
disease patients. But, the uncertainty in data was not removed
with the techniques available in data mining and implemented by
various authors. To remove uncertainty of unstructured data, an
attempt was made by introducing fuzziness in the measured data.
A membership function was designed and incorporated with the
measured value to remove uncertainty and fuzzified data was
used to predict the heart disease patients.. Further, an attempt
was made to classify the patients based on the attributes collected
from medical field. Minimum Euclidean distance Fuzzy K-NN
classifier was designed to classify the training and testing data
belonging to different classes. It was found that Fuzzy K-NN
classifier suits well as compared with other classifiers of
parametric techniques.

This paper aim is to average the use of techniques of decision trees, in combination with the management model CRISP-ADM, to help in the prediction of heart diseases. It is widely based on decision trees, an important concept in the field of artificial intelligence. This paper focuse on discussing how these trees are able to assist in the result making process of identifying heart diseases by the analysis of information provided from the hospitals. This information is captured with the help of techniques and the CRISP-DM management model of data mining in large prepared databases logged from hospital day to day transactions.