Knowledge Transfer between Humans and Conversational Agents: A Review, Organizing Framework, and Future Directions

Conversational agents (CAs) that use natural language to interact with humans are becoming ubiquitous in our daily lives. For CAs to perform effectively, knowledge transfer between human users and CAs is vital to complete tasks and to build common understanding with humans. While such knowledge transfer is important, relatively less research attention has been paid to it. Overall, we lack a systematic overview of how knowledge transfer can be facilitated between humans and CAs. Motivated thus, this article presents a literature review of empirical IS, HCI and Communications studies on the knowledge transfer between humans and CAs. We analyzed papers on this topic, synthesized the studies based on the antecedents, directions, processes, and outcomes of knowledge transfer. We contribute by providing a systematic understanding of research on knowledge transfer in human-CA interactions, proposing an organizing framework, identifying gaps in prior work, and outlining key future research directions

Trend Analysis of Belief-State History with Discrete Wavelet Transform for Improved Intention Discovery

Software Product Lines (SPL) have emerged as a new paradigm of software development. By means of mass production of customized software products, SPL has the potential to significantly reduce development time and cost while improving the quality of software systems. Currently, there is still a severe shortage of tools that support the decision-making process for software clients to interactively order software products due to the difficulty of software customization, especially via dialogue in natural language. While most of the existing approaches use POMDP-based dialogue management, this thesis research proposes to introduce historical information of belief states into the POMDP model and to analyze its trend with discrete wavelet transformation (DWT). Accordingly, a new algorithm is developed to improve the accuracy of intention discovery with trend analysis, and to reduce the dialog length by switching POMDP policies between contextual control modes according to the anticipated knowledge of different users. The efficiency and accuracy of the proposed method are examined by experiments with simulation

An Interactive Approach of Ontology-based Requirement Elicitation for Software Customization

Software product lines allow reusing a collection of related software engineering assets to develop custom-made high quality software with reduced time and cost. In this thesis, an interactive approach of requirement elicitation for software customization is presented. It first adopts an ontology-based requirement model to represent the commonalities and variabilities among a group of related requirement artefacts. The development of a dialogue system further enables users to interactively customize software products by means of text-based dialogue. While the ontology model directs the dialogue system to perform requirement elicitation, its instantiation is accomplished with the support of decomposition-based requirement refinement in Service-Oriented Architecture. Besides the design details, a case study is presented to demonstrate how customizing an online book shopping system could be achieved with interactive requirement elicitation. Finally, the reliability and efficiency of the proposed method are evaluated with experimental comparison

Robust Dialog Management Through A Context-centric Architecture

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users

Transformation of graphical models to support knowledge transfer

Menschliche Experten verfügen über die Fähigkeit, ihr Entscheidungsverhalten flexibel auf die jeweilige Situation abzustimmen. Diese Fähigkeit zahlt sich insbesondere dann aus, wenn Entscheidungen unter beschränkten Ressourcen wie Zeitrestriktionen getroffen werden müssen. In solchen Situationen ist es besonders vorteilhaft, die Repräsentation des zugrunde liegenden Wissens anpassen und Entscheidungsmodelle auf unterschiedlichen Abstraktionsebenen verwenden zu können. Weiterhin zeichnen sich menschliche Experten durch die Fähigkeit aus, neben unsicheren Informationen auch unscharfe Wahrnehmungen in die Entscheidungsfindung einzubeziehen. Klassische entscheidungstheoretische Modelle basieren auf dem Konzept der Rationalität, wobei in jeder Situation die nutzenmaximale Entscheidung einer Entscheidungsfunktion zugeordnet wird. Neuere graphbasierte Modelle wie Bayes\u27sche Netze oder Entscheidungsnetze machen entscheidungstheoretische Methoden unter dem Aspekt der Modellbildung interessant. Als Hauptnachteil lässt sich die Komplexität nennen, wobei Inferenz in Entscheidungsnetzen NP-hart ist. Zielsetzung dieser Dissertation ist die Transformation entscheidungstheoretischer Modelle in Fuzzy-Regelbasen als Zielsprache. Fuzzy-Regelbasen lassen sich effizient auswerten, eignen sich zur Approximation nichtlinearer funktionaler Beziehungen und garantieren die Interpretierbarkeit des resultierenden Handlungsmodells. Die Übersetzung eines Entscheidungsmodells in eine Fuzzy-Regelbasis wird durch einen neuen Transformationsprozess unterstützt. Ein Agent kann zunächst ein Bayes\u27sches Netz durch Anwendung eines in dieser Arbeit neu vorgestellten parametrisierten Strukturlernalgorithmus generieren lassen. Anschließend lässt sich durch Anwendung von Präferenzlernverfahren und durch Präzisierung der Wahrscheinlichkeitsinformation ein entscheidungstheoretisches Modell erstellen. Ein Transformationsalgorithmus kompiliert daraus eine Regelbasis, wobei ein Approximationsmaß den erwarteten Nutzenverlust als Gütekriterium berechnet. Anhand eines Beispiels zur Zustandsüberwachung einer Rotationsspindel wird die Praxistauglichkeit des Konzeptes gezeigt.Human experts are able to flexible adjust their decision behaviour with regard to the respective situation. This capability pays in situations under limited resources like time restrictions. It is particularly advantageous to adapt the underlying knowledge representation and to make use of decision models at different levels of abstraction. Furthermore human experts have the ability to include uncertain information and vague perceptions in decision making. Classical decision-theoretic models are based directly on the concept of rationality, whereby the decision behaviour prescribed by the principle of maximum expected utility. For each observation some optimal decision function prescribes an action that maximizes expected utility. Modern graph-based methods like Bayesian networks or influence diagrams make use of modelling. One disadvantage of decision-theoretic methods concerns the issue of complexity. Finding an optimal decision might become very expensive. Inference in decision networks is known to be NP-hard. This dissertation aimed at combining the advantages of decision-theoretic models with rule-based systems by transforming a decision-theoretic model into a fuzzy rule-based system. Fuzzy rule bases are an efficient implementation from a computational point of view, they can approximate non-linear functional dependencies and they are also intelligible. There was a need for establishing a new transformation process to generate rule-based representations from decision models, which provide an efficient implementation architecture and represent knowledge in an explicit, intelligible way. At first, an agent can apply the new parameterized structure learning algorithm to identify the structure of the Bayesian network. The use of learning approaches to determine preferences and the specification of probability information subsequently enables to model decision and utility nodes and to generate a consolidated decision-theoretic model. Hence, a transformation process compiled a rule base by measuring the utility loss as approximation measure. The transformation process concept has been successfully applied to the problem of representing condition monitoring results for a rotation spindle

A dynamic multi-application dialog engine for task-oriented voice user interfaces

This thesis introduces the Dymalog framework for spoken language dialog systems, which separates the applications from the actual dialog system. It facilitates the control of a plurality of applications through a single dialog system, changeable during run time. This is achieved by application-independent knowledge processing inside the dialog system, based on a hierarchical representation of obtained information (o²I -Trees). The approach enables the realization of generic dialog functionalities. Dymalog is composed of a collection of components; each serves mainly a single purpose. It fosters the generation of competing hypotheses during the processing of the user input in order to derive an optimal interpretation at a certain stage in the processing. The Marvin dialog system puts Dymalog into practice. We discuss selected interactions with various applications enabled for the operation through the system. The parameterized hypothesis selection process is considered in detail, especially the parameter estimation algorithm Grail, and the same holds for the development process in the generation of competing hypotheses for the user input.Die Arbeit stellt die Grundlagen zur Realisierung des sprachbasierten Dialogsystems Marvin für die Interaktion eines Benutzers mit verschiedenen Applikationen vor: Dymalog. Es erlaubt die Kontrolle unterschiedlicher Applikationen durch ein einziges System und ermöglicht u.a. dynamische Änderungen der verfügbaren Applikationen zur Laufzeit. Dies wird durch applikationsunabhängige Wissensverarbeitung erreicht, basierend auf modularen ontologischen Beschreibungen der Anwendungsfreiheitsgrade (o²I -Trees). Die Trennung von Dialogsystem und Applikationen ermöglicht die Realisierung generischer Dialogfunktionalitäten. Dymalog besteht aus einer Reihe von separaten Einheiten, jede beinhaltet im Wesentlichen ein Modell zur Verarbeitung der Benutzereingabe. Um die optimale Interpretation der Benutzereingabe zu erlangen wird die Generation alternativer Interpretationen gefördert. Das Marvin Dialogsystem realisiert die Konzepte aus Dymalog. Ausgewählte Interaktionen mit verschiedenen Applikationen werden diskutiert. Ferner wird der parameterisierte Auswahlprozeß der \u27besten\u27; Interpretation beleuchtet, insbesondere der Parameter-Schätzalgorithmus Grail, und die Erzeugung alternativer Hypothesen durch ausgewählte Einheiten diskutiert

Dialogue and Domain Knowledge Management in Dialogue Systems

Intelligent dialogue systems must be able to respond properly to a variety of requests involving knowledge of the dialogue, the task at hand, and the domain. This requires advanced knowledge reasoning performed by various processing modules. We argue that it is important to understand the nature of the various reasoning mechanisms involved and to separate not only, for instance, interpretation, generation, and dialogue management but also domain knowledge and task reasoning. This facilitates portability of the dialogue system to new domains and makes it easier to enhance its capabilities. In this paper we will focus on the dialogue and domain knowledge reasoning components and show how they can cooperate to achieve natural interaction.