A Spoken Dialogue System's (SDS's) dialogue strategy specifies which action it will take depending on its representation of the current dialogue context. Designing it by hand involves anticipating how users will interact with the system, and/or repeated testing and refining, and so can be a difficult, time-consuming task. Since SDSs inevitably make understanding errors, a particularly important issue is how to design ``repair strategies'', the parts of the dialogue strategy which attempt to get the dialogue ``back-on-track'' following these errors. To try to produce better dialogue strategies with less time and effort, previous researchers have modelled a dialogue strategy as a sequential decision problem called a Markov Decision Process (MDP), and then applied Reinforcement Learning (RL) algorithms to example training dialogues to generate dialogue strategies automatically. More recent research has used training dialogues conducted with simulated rather than real users and learned which action to take in all dialogue contexts, (a ``full'' as opposed to a ``partial'' dialogue strategy) - simulated users allow more training dialogues to be generated, and the exploration of new dialogue contexts not present in an original dataset. As yet however, limited insight has been provided as to which dialogue contextual features are important to include in the MDP and why. Indeed, a full dialogue strategy has not been learned from training dialogues with a realistic probabilistic user simulation derived from real user data, and then shown to work well with real users. This thesis investigates the value of adding new linguistically-motivated contextual features to the MDP when using RL to learn full dialogue strategies for SDSs. These new features are recent Dialogue Acts (DAs). DAs indicate the role or intention of an utterance in a dialogue e.g. ``provide-information'', an utterance being a complete unit of a speaker's speech, often bounded by silence. An accurate probabilistic user simulation learned from real user data is used for generating training dialogues, and the recent DAs are shown to improve performance in testing in simulation and with real users. With real users, performance is also better than other competing learned and hand-crafted strategies. Analysis of the strategies, and further simulation experiments show how the DAs improve performance through better repair strategies. The main findings are expected to apply to SDSs in general - indeed our strategies are learned and tested on real users in different domains, (flight-booking versus tourist information). Comparisons are also made to recent research which focuses on handling understanding errors in SDSs, but which does not use RL or user simulations.

Conversational agents can benefit healthcare across different application domains. However, the automated generation of reliable agents is still challenging and lags behind traditional conversational domains. This research exploited the interplay of information management and automated planning to efficiently model the expected behavior of goal-oriented health dialogues. The proposed approach supports the dynamic generation of predictable policies that are used for the management of the health dialogue as well as the identification of the dialogue state. This work advances the state of the art in health dialogue management by automating the generation (and update) of efficient dialogue managers with a reduced cost since they do not require handcrafting of the dialogue policy or large conversational datasets.

In this thesis, the design and use of ontologies as domain knowledge sources in information-providing dialogue systems are investigated. The research is divided into two parts, theoretical investigations that have resulted in a requirements specifications on the design of ontologies to be used in information-providing dialogue systems, and empirical work on the development of a framework for use of ontologies in information-providing dialogue systems. The framework includes three models: A model for ontology-based semantic analysis of questions. A model for ontology-based dialogue management, specifically focus management and clarifications. A model for ontology-based domain knowledge management, specifically transformation of user requests to system oriented concepts used for information retrieval. In this thesis, it is shown that using ontologies to represent and reason on domain knowledge in dialogue systems has several advantages. A deeper semantic analysis is possible in several modules and a more natural and efficient dialogue can be achieved. Another important aspect is that it facilitates portability; to be able to reuse adapt the dialogue system to new tasks and domains, since the domain-specific knowledge is separated form generic features in the dialogue system architecture. Other advantages are that it reduces the complexity of linguistic produced in various domains.

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.

<p>In this thesis a new concept called domain knowledge management for informationproviding dialogue systems is introduced. Domain knowledge management includes issues related to representation and use of domain knowledge as well as access of background information sources, issues that previously have been incorporated in dialogue management.</p><p>The work on domain knowledge management reported in this thesis can be divided in two parts. On a general theoretical level, knowledge sources and models used for dialogue management, including domain knowledge management, are studied and related to the capabilities they support. On a more practical level, domain knowledge management is examined in the contexts of a dialogue system framework and a specific instance of this framework, the ÖTRAF system. In this system domain knowledge management is implemented in a separate module, a Domain Knowledge Manager.</p><p>The use of a specialised Domain Knowledge Manager has a number of advantages. The first is that dialogue management becomes more focused as it only has to consider dialogue phenomena, while domain-specific reasoning is handled by the Domain Knowledge Manager. Secondly, porting of a system to new domains is facilitated since domain-related issues are separated out in specialised domain knowledge sources. The third advantage with a separate module for domain knowledge management is that domain knowledge sources can be easily modified, exchanged, and reused.</p> / Report code: LiU-Tek-Lic-2001:27.

Recent advancements in the area of spoken language processing and the wide acceptance of portable devices, have attracted signicant interest in spoken dialogue systems.These conversational systems are man-machine interfaces which use natural language (speech) as the medium of interaction.In order to conduct dialogues, computers must have the ability to decide when and what information has to be exchanged with the users. The dialogue management module is responsible to make these decisions so that the intended task (such as ticket booking or appointment scheduling) can be achieved.Thus learning a good strategy for dialogue management is a critical task.In recent years reinforcement learning-based dialogue management optimization has evolved to be the state-of-the-art. A majority of the algorithms used for this purpose needs vast amounts of training data.However, data generation in the dialogue domain is an expensive and time consuming process. In order to cope with this and also to evaluatethe learnt dialogue strategies, user modelling in dialogue systems was introduced. These models simulate real users in order to generate synthetic data.Being computational models, they introduce some degree of modelling errors. In spite of this, system designers are forced to employ user models due to the data requirement of conventional reinforcement learning algorithms can learn optimal dialogue strategies from limited amount of training data when compared to the conventional algorithms. As a consequence of this, user models are no longer required for the purpose of optimization, yet they continue to provide a fast and easy means for quantifying the quality of dialogue strategies. Since existing methods for user modelling are relatively less realistic compared to real user behaviors, the focus is shifted towards user modelling by means of inverse reinforcement learning. Using experimental results, the proposed method's ability to learn a computational models with real user like qualities is showcased as part of this work.

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.

There is an overwhelming variation in the ways an intelligent agent can rationalize communication with a conversational partner. This variation presents many incompatibilities that lead to the specialization of conversational capabilities. This has produced a plethora of models and ideas on how an intelligent agent should understand, interact with, and incorporate communication from a human conversational participant. This dissertation approaches this problem with the thesis that there exists a language between that of human natural language and the behavioral reasoning of an intelligent agent, and that this language is capable of not only unifying the various models used in literature, but also provides the foundation for a theoretical framework for an engineering methodology for building such models. A theory of practical communication language is developed, including the introduction of the meaning-action concept, an expressive and powerful representation based on speech-act and dialogue-act theories, but extended with notions of behavioral operators as well as signatures that allow the operators to incorporate structured and well-defined concepts. An engineering methodology is presented for the construction of concepts, operators and rules that create the language and model of a specific domain, including methodology for the verification and validation of that language and model. The resultant practical communication language methodology, based on the combination of rational communication and meaning-action concepts, will introduce several major enhancements to dialogue management. These enhancements include the use of meaning-action concepts as a shared medium and the introduction of a shared concept graph. This methodology will be used along with various dialogue models from human-human, human-agent and agent-agent communication to construct a task-oriented language and model called the task communication language framework. This framework is then implemented within an intelligent agent in a real-time resource management simulation. A sample output listing from actual human interaction with that implementation is used to demonstrate that the resulting framework does indeed incorporate many of the disparate models of communication and their corresponding capabilities including command and control, information seeking, notification and bother, clarification, explanation, discussion, negotiation, mutual planning, interruption, feedback, adjustable autonomy and corrective dialogues.