DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> This paper discusses the problems of representing and reasoning with information about knowledge and action. The first section discusses the importance of having systems that understand the concept of knowledge, and how knowledge is related to action. Section 2 points out some of the special problems that are involved in reasoning about knowledge, and section S presents a logic of knowledge based on the idea of possible worlds. Section 4 integrates this with a logic of actions and gives an example of reasoning in the combined system. Section 5 makes some concluding comments. <s> BIB001 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> This paper presents an approach for providing patients with personalised explanations of their medical record. Simple text planning techniques are used to construct relevant explanations based on information in the record and information in a general medical knowledge base. We discuss the results of the evaluation of our system with diabetes patients at three diabetes clinics in Scotland. <s> BIB002 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> We present the basic components of ARTIMIS, a rational agent based on the implementation of a formal theory of interaction. This theory involves a set of generic axioms which models, in a homogeneous logical framework, principles of rational behaviour, communication, and cooperation. It thus supports the rational unit of an autonomous communicating agent, which, in our case, is the kernel of a cooperative spoken dialogue system. It is expressed in a first-order (multi)modal logic of mental attitudes (belief, uncertainty, and intention) and actions. The implementation consists of an inference engine, which is a theorem prover, specifically designed to reason using the axioms and rules of this kind of logical theory in their syntactical form. <s> BIB003 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> Users of computing devices are increasingly likely to be subject to situationally determined distractions that produce exceptionally high cognitive load. The question arises of how a system can automatically interpret symptoms of such cognitive load in the user’s behavior. This paper examines this question with respect to systems that process speech input. First, we synthesize results of previous experimental studies of the ways in which a speaker’s cognitive load is reflected in features of speech. Then we present a conceptualization of these relationships in terms of Bayesian networks. For two examples of such symptoms—sentence fragments and articulation rate—we present results concerning the distribution of the symptoms in realistic assistance dialogs. Finally, using artificial data generated in accordance with the preceding analyses, we examine the ability of a Bayesian network to assess a user’s cognitive load on the basis of limited observations involving these two symptoms. <s> BIB004 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> This paper describes briefly an approach for tailoring the content of automatically generated hypertext explanations. The implemented pilot system hylite+ has a dynamically updated user model, which is used by the language generation modules to adapt the explanations to the user beliefs and preferences. The goal is to provide sufficiently detailed information which, on one hand helps the user by explaining the unknown terms, and on the other, avoids repeating already known facts. <s> BIB005 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> An anesthetic vaporizer is disclosed having a linear flow system which insures precise predictability of the desired anesthetic composition. The total flow into the vaporizer is divided into two main gas streams, the first gas stream proceeds through precise linear flow restrictors and is then subdivided into two gas substreams by a concentration valve. One of the substreams is directed by the concentration valve through a vaporizing chamber where it becomes saturated with anesthetic and then to the vaporizer outlet. The other substream is channeled past the concentration valve directly to the vaporizer outlet where it is reunited with the one substream. By adjusting the concentration valve, the ratio of the substreams may be changed, thus affecting the eventual concentration of the anesthetic composition from the vaporizer outlet. The second main gas stream proceeds through a thermal compensation valve and is subdivided into two substreams, one of which is directed through fixed linear flow restrictors to the vaporizer outlet, and the other substream is directed through a variable thermal bypass also having linear flow restrictors. The flow through the variable thermal bypass to the vaporizer outlet is varied in accordance with the sensed temperature in the anesthetic vaporizing chamber. <s> BIB006 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. Unlike previous studies that focus on user's knowledge or typical kinds of users, the user model we propose is more comprehensive. Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree of hastiness. Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. We obtained reasonable classification accuracy for all dimensions. Dialogue strategies based on the user modeling are implemented in Kyoto city bus information system that has been developed at our laboratory. Experimental evaluation shows that the cooperative responses adaptive to individual users serve as good guidance for novice users without increasing the dialogue duration for skilled users. <s> BIB007 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users' requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research. <s> BIB008 </s> DOI: 10.1017/S000000000000000 Printed in the United Kingdom A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies <s> User Modelling in Spoken Dialogue Systems and Natural Language Processing <s> Individuals differ in the resources that they are willing to expend on information gathering and on the importance of different kinds of information. We have developed MADSUM, a system that takes into account user constraints on resources, the significance of propositions that might be included in a response, and the user's priorities with respect to resource and content attributes; MADSUM produces a response tailored to individual users in a decision support setting. <s> BIB009
As mentioned in the introduction to this paper, research on User Modelling has a long history in the field of Natural Language Processing (NLP) and Spoken Dialogue Systems (SDS), particularly in areas concerned with the design of adaptive and adaptable systems. Traditionally, the majority of work on User Modelling in NLP and SDS is linguistically motivated and of a non-statistical nature. The aim of research in this area is typically to construct a representative model of the individual user in order to describe his or her state during a course of interaction with the system. In a spoken dialogue environment this may include information about the user's goal or knowledge regarding previously exchanged pieces of information. Depending on the application domain, the model can also include the user's preferences, knowledge about the subject matter, beliefs, capabilities, level of satisfaction with the system behaviour and so on. The inspiration for building user models for use in spoken dialogue systems originally stems from research on human-human interaction. People intuitively use models of their dialogue partners when they engage in a conversation. The style of language and level of formality, for instance, depends on the mental model of the interlocutor. Our mental model can also help us to understand the dialogue partner's intentions, even if they have not been well articulated or worded in a precise manner. In a similar fashion a dialogue system can benefit from consulting a user model. A recent review on the "Synergies and Limitations" of User Modelling and NLP by Zukerman and Litman BIB008 shows that research has investigated the use of user models in almost all subfields of spoken dialogue systems. Particularly active areas are Natural Language Generation (NLG) and Natural Language Understanding (NLU). In NLG, content generators can consult user models to produce output which is tailored to specific attributes of the individual user. Examples of work in this area demonstrate how systems can attempt to match characteristics such as expertise and interests, or domain-specific information such as medical records, investment portfolios, etc.   BIB002 BIB005 BIB009 . In NLU, user modelling often takes the form of plan recognition. The aim here is to make inferences about a user's preferences and goals in order to (for example) respond better to ill-formed queries BIB006 or to detect expressions of doubt  . According to Zukerman and Litman, user modelling is less frequently used in dialogue management. Very early examples of research in this area are dominated by knowledge-based formalisms and various types of logic aimed at modelling the complex beliefs and intentions of agents BIB001   BIB003 . In more recent years, dialogue systems have tended to focus on cooperative, task-oriented rather than conversational forms of dialogue so that user models are now typically less complex. User models in dialogue management now often take the form of simple rules or attribute-value pairs BIB007 . As in NLG and NLU, the general aim is usually to tailor the behaviour to specific user attributes. These can be either fixed attributes, such as "a preference for afternoon meetings"  or dynamic attributes, such as the cognitive load on a user during a conversation BIB004 .