User modeling in dialog systems: Potentials and hazards (original) (raw)
Related papers
1990
This chapter surveys the field of user modeling in artificial intelligence dialog systems. First, reasons why user modeling has become so important in the last few years are pointed out, and definitions are proposed for the terms 'user model' and 'user modeling component'. Research within and outside of artificial intelligence which is related to user modeling in dialog systems is discussed. In Section 2, techniques for constructing user models in the course of a dialog are presented and, in Section 3, recent proposals for representing a wide range of assumptions about a user's beliefs and goals in a system's knowledge base are surveyed. Examples for the application of user models in systems developed to date are then given, and some social implications discussed. Finally, unsolved problems like coping with collective beliefs or resource-limited processes are investigated, and prospects for applicationoriented research are outlined. Although the survey is restricted to user models in naturallanguage dialog systems, most of the concepts and methods discussed can be extended to AI dialog systems in general.
Dialog Systems and their Inputs
One of the main limitations in existent domain-independent conver- sational agents is that the general and linguistic knowledge of these agents is limited to what the agents' developers explicitly defined. Therefore, a system which analyses user input at a deeper level of abstraction which backs its knowledge with common sense information will essentially result in a system that is capable of providing more adequate responses which in turn result in a better overall user experience. From this premise, a framework was proposed, and a working prototype was implemented upon this framework. These make use of various natural language processing tools, online and offline knowledge bases, and other information sources, to enable it to comprehend and construct relevant responses.
User Modeling in Spoken Dialogue Systems to Generate Flexible Guidance
User Modeling and User-Adapted Interaction, 2005
We address the issue of appropriate user modeling to generate cooperative responses to users in spoken dialogue systems. Unlike previous studies that have focused on a user's knowledge, we propose more generalized modeling. We specifically set up three dimensions for user models: the skill level in use of the system, the knowledge level about the target domain, and the degree of urgency. Moreover, the models are automatically derived by decision tree learning using actual dialogue data collected by the system. We obtained reasonable accuracy in classification for all dimensions. Dialogue strategies based on user modeling were implemented on the Kyoto City Bus Information System that was developed at our laboratory. Experimental evaluations revealed that the cooperative responses adapted to each subject type served as good guides for novices without increasing the duration dialogue lasted for skilled users.
User modelling, dialog structure, and dialog strategy in HAM-ANS
Proceedings of the second conference on European chapter of the Association for Computational Linguistics -, 1985
AI dialog systems are now developing from question-answering systems toward advising systems. This includes: discussed here, but see (Jameson, Wahlster 1982). The second part of this paper presents user modelling with respect to a dialog strategy which selects and verbalizes the appropriate speech act of recommendation.-structuring dialog-understanding and generating a wider range of speech acts than simply information request and answer user modelling User modelling in HAM-ANS is closely connected to dialog structure and dialog strategy. In advising the user, the system generates and verbalizes speech acts. The choice of the speech act is guided by the user profile and the dialog strategy of the system.
User modeling in spoken dialogue systems for flexible guidance generation
… Conference on Speech …, 2003
We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. Unlike previous studies that focus on users' knowledge or typical kinds of users, the proposed user model is more comprehensive. Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and degree of hastiness. Moreover, the models are automatically derived by decision tree learning using real dialogue data. 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.
User Modeling and User-adapted Interaction, 1991
This article investigates the implications ofactive user model acquisition upon plan recognition, domain planning, and dialog planning in dialog architectures. A dialog system performs active user model acquisition by querying the user during the course of the dialog. Existing systems employ passive strategies that rely on inferences drawn from passive observation of the dialog. Though passive acquisition generally reduces unnecessary dialog, in some cases the system can effectively shorten the overall dialog length by selectively initiating subdialogs for acquiring information about the user. We propose a theory identifying conditions under which the dialog system should adoptactive acquisition goals. Active acquisition imposes a set ofrationality requirements not met by current dialog architectures. To ensure rational dialog decisions, we propose significant extensions to plan recognition, domain planning, and dialog planning models, incorporating decision-theoretic heuristics for expected utility. The most appropriate framework for active acquisition is a multi-attribute utility model wherein plans are compared along multiple dimensions of utility. We suggest a general architectural scheme, and present an example from a preliminary implementation.
1986
Abstract The paper investigates several approaches to user modeling in natural-language dialogue systems.
User Modelling in Adaptive Dialogue Management
This paper describes an adaptive approach to dialogue management in spoken dialogue systems. The system maintains a user model, in which assumptions about the user's expectations of the system are recorded. Whenever errors occur, the dialogue manager drops some assumption from the user model, and adapts its behaviour accordingly. The system uses a hierarchical slot structure that allows generation and interpretation of utterances at various levels of generality. This is essential for exploiting mixed-initiative to optimise effectiveness. The hierarchical slot structure is also a source of variation. This is important because it is ineffective to repeat prompts in cases of speech recognition errors, for in such cases users are likely to repeat their responses also, and thus the same speech recognition problems too.
Designing Model-Based Intelligent Dialogue Systems
Information Modeling in the New Millennium, 2001
Intelligent Systems are served by Intelligent User Interfaces aimed to improve the efficiency, effectiveness and adaptation of the interaction between the user and the computer by representing, understanding and implementing models. The Intelligent User Interface Model (IUIM) helps to design and develop Intelligent Systems considering its architecture and its behavior. It focuses the Interaction and Dialogue between User and System at the heart of an Intelligent Interactive System. An architectural model, which defines the components of the model, and a conceptual model, which relates to its contents and behavior, compose the IUIM. The conceptual model defines three elements: an Adaptive User Model (including components for building and updating the user model), a Task Model (including general and domain specific knowledge) and an Adaptive Discourse Model (to be assisted by an intelligent help and a learning module). We will show the implementation of the model by describing an application named Stigma -A STereotypical Intelligent General Matching Agent for Improving Search Results on the Internet. Finally, we compared the new model with others, stating the differences and the advantages of the proposed model.