Personal assistants need to allow the user to interact with the system in a flexible and adaptive way such as through spoken language dialogue. This research is aimed at achieving robust and effective dialogue management in such applications. We focus on an application, the Smart Personal Assistant (SPA), in which the user can use a variety of devices to interact with a collection of personal assistants, each specializing in a task domain. The current implementation of the SPA contains an e-mail management agent and a calendar agent that the user can interact with through a spoken dialogue and a graphical interface on PDAs. The user-system interaction is handled by a Dialogue Manager agent. We propose an agent-based approach that makes use of a BDI agent architecture for dialogue modelling and control. The Dialogue Manager agent of the SPA acts as the central point for maintaining coherent user-system interaction and coordinating the activities of the assistants. The dialogue model consists of a set of complex but modular plans for handling communicative goals. The dialogue control flow emerges automatically as the result of the agent???s plan selection by the BDI interpreter. In addition the Dialogue Manager maintains the conversational context, the domainspecific knowledge and the user model in its internal beliefs. We also consider the problem of dialogue adaptation in such agent-based dialogue systems. We present a novel way of integrating learning into a BDI architecture so that the agent can learn to select the most suitable plan among those applicable in the current context. This enables the Dialogue Manager agent to tailor its responses according to the conversational context and the user???s physical context, devices and preferences. Finally, we report the evaluation results, which indicate the robustness and effectiveness of the dialogue model in handling a range of users.

Personal assistants need to allow the user to interact with the system in a flexible and adaptive way such as through spoken language dialogue. This research is aimed at achieving robust and effective dialogue management in such applications. We focus on an application, the Smart Personal Assistant (SPA), in which the user can use a variety of devices to interact with a collection of personal assistants, each specializing in a task domain. The current implementation of the SPA contains an e-mail management agent and a calendar agent that the user can interact with through a spoken dialogue and a graphical interface on PDAs. The user-system interaction is handled by a Dialogue Manager agent. We propose an agent-based approach that makes use of a BDI agent architecture for dialogue modelling and control. The Dialogue Manager agent of the SPA acts as the central point for maintaining coherent user-system interaction and coordinating the activities of the assistants. The dialogue model consists of a set of complex but modular plans for handling communicative goals. The dialogue control flow emerges automatically as the result of the agent???s plan selection by the BDI interpreter. In addition the Dialogue Manager maintains the conversational context, the domainspecific knowledge and the user model in its internal beliefs. We also consider the problem of dialogue adaptation in such agent-based dialogue systems. We present a novel way of integrating learning into a BDI architecture so that the agent can learn to select the most suitable plan among those applicable in the current context. This enables the Dialogue Manager agent to tailor its responses according to the conversational context and the user???s physical context, devices and preferences. Finally, we report the evaluation results, which indicate the robustness and effectiveness of the dialogue model in handling a range of users.

Personal assistants need to allow the user to interact with the system in a flexible and adaptive way such as through spoken language dialogue. This research is aimed at achieving robust and effective dialogue management in such applications. We focus on an application, the Smart Personal Assistant (SPA), in which the user can use a variety of devices to interact with a collection of personal assistants, each specializing in a task domain. The current implementation of the SPA contains an e-mail management agent and a calendar agent that the user can interact with through a spoken dialogue and a graphical interface on PDAs. The user-system interaction is handled by a Dialogue Manager agent. We propose an agent-based approach that makes use of a BDI agent architecture for dialogue modelling and control. The Dialogue Manager agent of the SPA acts as the central point for maintaining coherent user-system interaction and coordinating the activities of the assistants. The dialogue model consists of a set of complex but modular plans for handling communicative goals. The dialogue control flow emerges automatically as the result of the agent???s plan selection by the BDI interpreter. In addition the Dialogue Manager maintains the conversational context, the domainspecific knowledge and the user model in its internal beliefs. We also consider the problem of dialogue adaptation in such agent-based dialogue systems. We present a novel way of integrating learning into a BDI architecture so that the agent can learn to select the most suitable plan among those applicable in the current context. This enables the Dialogue Manager agent to tailor its responses according to the conversational context and the user???s physical context, devices and preferences. Finally, we report the evaluation results, which indicate the robustness and effectiveness of the dialogue model in handling a range of users.

Personal assistants need to allow the user to interact with the system in a flexible and adaptive way such as through spoken language dialogue. This research is aimed at achieving robust and effective dialogue management in such applications. We focus on an application, the Smart Personal Assistant (SPA), in which the user can use a variety of devices to interact with a collection of personal assistants, each specializing in a task domain. The current implementation of the SPA contains an e-mail management agent and a calendar agent that the user can interact with through a spoken dialogue and a graphical interface on PDAs. The user-system interaction is handled by a Dialogue Manager agent. We propose an agent-based approach that makes use of a BDI agent architecture for dialogue modelling and control. The Dialogue Manager agent of the SPA acts as the central point for maintaining coherent user-system interaction and coordinating the activities of the assistants. The dialogue model consists of a set of complex but modular plans for handling communicative goals. The dialogue control flow emerges automatically as the result of the agent???s plan selection by the BDI interpreter. In addition the Dialogue Manager maintains the conversational context, the domainspecific knowledge and the user model in its internal beliefs. We also consider the problem of dialogue adaptation in such agent-based dialogue systems. We present a novel way of integrating learning into a BDI architecture so that the agent can learn to select the most suitable plan among those applicable in the current context. This enables the Dialogue Manager agent to tailor its responses according to the conversational context and the user???s physical context, devices and preferences. Finally, we report the evaluation results, which indicate the robustness and effectiveness of the dialogue model in handling a range of users.