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

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