Los modelos de diálogo y sus aplicaciones en sistemas de diálogo hombre-máquina: revisión de la literatura

Un proceso de diálogo entre humanos involucra una serie de actos del habla, cuya finalidad es transmitir los deseos, intenciones y creencias entre las partes involucradas en el mismo. El reconocimiento y clasificación de los actos del habla, la construcción de modelos basados en estos actos del habla y la evaluación de los modelos construidos, es el objetivo de los modelos de diálogo. Además, estos modelos, incorporados en un sistema informático, permiten la interacción hombre-máquina usando el habla para la solución de diversos problemas cotidianos como: comprar un tiquete de tren, reservar un vuelo, etc. En este artículo se recogen las diferentes técnicas para la construcción de modelos de diálogo y algunos de los diversos sistemas informáticos que surgieron a partir de ellos, con el fin de determinar la aplicabilidad de los modelos de diálogo en el proceso de captura de requisitos durante la fase de definición del ciclo de vida de una aplicación de software

A modified approach of POMDP-based dialogue management

This thesis applies the theory of history information space for a thorough study of dialogue management in major approaches, ranging from the classical approach based upon finite state machine to the most recent approach using partially observable Markov decision process (PODMP). While most of the approaches use various techniques to estimate system state, the POMDP-based approach avoids state estimation and uses belief state for decision making. In addition, it provides a mechanism to model uncertainty and allows for errorrecovery. PODMP-based dialogue management demonstrates undeniable advantages in the handling of input uncertainty over all the other approaches. However, applying Markovian over the belief-state space in the current POMDP models causes significant loss of valuable information in dialogue history, leading to untruthful recognition of user\u27s intention. To improve the performance of POMDP-based dialogue management this thesis introduces belief history into the planning process, and uses not only the current but also the previous belief state for the determination of actions. In the new approach, all changes of belief state require a validation with domain constraints, and an invalid change results in a modification to the actions provided by the POMDP solver. Experiments show that this new approach is able to handle uncertainty caused by user\u27s lack of domain knowledge and practical constraints, thus becoming more accurate in intention recognition

A task ontology model for domain independent dialogue management

Dialogue systems have been a rapidly growing area in both scientific research and commercial application since 1990s. They can be applied in various fields, such as business, healthcare and education, etc. Due to its complexity, the design and development of a dialogue system is time consuming and costly. It is highly desirable for a generic dialogue system, especially dialogue management that is independent of specific domains. Methods or architecture for domain independent dialogue systems have been proposed by previous research in literature, however each of them has its own limitations and none has been widely adopted. This paper presents a new approach, a task ontology model for domain independent dialogue management. An abstract task ontology model is developed and based on this model a generic dialogue manager is created. Knowledge about a specific task is modeled in its task ontology and retrieved by an ontology reasoning component situated in the dialogue manager. Thus the dialogue manager is task or domain independent. A dialogue system is developed based on the proposed method and experimented with two different tasks: the book borrowing and the online train ticket booking. The experiment results indicate that the dialogue system can be readily applied to tasks from different domains without any modification. This paper has implications on future research and development of domain independent dialogue systems. It also contributes to the knowledge and dialogue system reuse and will have impact on the application of dialogue systems in a wider range of areas

Scalability and Portability of a Belief Network-based Dialog Model for Different Application Domains

This paper describes the scalability and portability of a Belief Network (BN)-based mixed initiative dialog model across application domains. The Belief Networks (BNs) are used to automatically govern the transitions between a systeminitiative and a user-initiative dialog model, in order to produce mixed-initiative interactions. We have migrated our dialog model from a simpler domain of foreign exchange to a more complex domain of air travel information service. The adapted processes include: (i) automatic selection of specified concepts in the user's query, for the purpose of informational goal inference; (ii) automatic detection of missing / spurious concepts based on backward inference using the BN. We have also enhanced our dialog model with the capability of discourse context inheritance. To ease portability across domains, which often implies the lack of training data for the new domain, we have developed a set of principles for handassigning BN probabilities, based on the "degree of belief" in the relationships between concepts and goals. Application of our model to the ATIS data gave promising results. 1

Scalability and portability of a belief network-based dialog model for different application domains

This paper describes the scalability and portability of a Belief Network (BN)-based mixed initiative dialog model across application domains. The Belief Networks (BNs) are used to automatically govern the transitions between a system-initiative and a user-initiative dialog model, in order to produce mixedinitiative interactions. We have migrated our dialog model from a simpler domain of foreign exchange to a more complex domain of air travel information service. The adapted processes include: (i) automatic selection of specified concepts in the user’s query, for the purpose of informational goal inference; (ii) automatic detection of missing / spurious concepts based on backward inference using the BN. We have also enhanced our dialog model with the capability of discourse context inheritance. To ease portability across domains, which often implies the lack of training data for the new domain, we have developed a set of principles for hand-assigning BN probabilities, based on the “degree of belief ” in the relationships between concepts and goals. Application of our model to the ATIS data gave promising results. 1

An Approach for Contextual Control in Dialogue Management with Belief State Trend Analysis and Prediction

This thesis applies the theory of naturalistic decision making (NDM) in human physcology model for the study of dialogue management system in major approaches from the classical approach based upon finite state machine to most recent approach using partially observable markov decision process (POMDP). While most of the approaches use various techniques to estimate system state, POMDP-based system uses the belief state to make decisions. In addition to the state estimation POMDP provides a mechanism to model the uncertainty and allows error-recovery. However, applying Markovian over the belief-state space in the current POMDP models cause significant loss of valuable information in the dialogue history, leading to untruthful management of user\u27s intention. Also there is a need of adequate interaction with users according to their level of knowledge. To improve the performance of POMDP-based dialogue management, this thesis proposes an enabling method to allow dynamic control of dialogue management. There are three contributions made in order to achieve the dynamism which are as follows: Introduce historical belief information into the POMDP model, analyzing its trend and predicting the user belief states with history information and finally using this derived information to control the system based on the user intention by switching between contextual control modes. Theoretical derivations of proposed work and experiments with simulation provide evidence on dynamic dialogue control of the agent to improve the human-computer interaction using the proposed algorithm