Showing papers on "User modeling published in 1988"

Towards a Practical GOMS Model Methodology for User Interface Design

Abstract: Publisher Summary This chapter presents a practical GOMS model methodology for user interface design. The basic approach to user-interface design using the cognitive complexity approach would be that the iterative design process would be followed, but with the evaluation of a proposed design being done with simulation techniques rather than actual human user testing; only a final test of the design would require actual user testing. Additional user testing would be involved to develop aspects of the design, such as screen layout, that are not directly addressed by an analysis of the procedures entailed by the design. There are several problems in using the cognitive complexity approach as a design tool that have become clear from technology transfer. The chapter presents two critical problems: (1) the difficulty of constructing production rule simulation models, and (2) the difficulty of doing, in a standardized and reliable way, the detailed task analysis required to construct the representation of the procedural knowledge that the user should have to operate the system.

Tailoring object descriptions to a user's level of expertise

Abstract: A question answering program providing access to a large amount of data will be most useful if it can tailor its answers to each individual user. In particular, a user's level of knowledge about the domain of discourse is an important factor in this tailoring if the answer provided is to be both informative and understandable to the user. In this research, we address the issue of how the user's domain knowledge can affect an answer. By studying texts, we found that the user's level of domain knowledge affected the kind of information provided and not just the amount of information, as was previously assumed. Depending on the user's assumed domain knowledge, a description can be either parts-oriented or process-oriented. Thus the user's level of expertise in a domain can guide a system in choosing the appropriate facts from the knowledge base to include in an answer. We propose two distinct descriptive strategies that can be used in a question answering program, and show how they can be mixed to include the appropriate information from the knowledge base, given the user's domain knowledge. We have implemented these strategies in TAILOR, a computer system that generates descriptions of devices. TAILOR uses one of the two discourse strategies identified in texts to construct a description for either a novice or an expert. It can merge the strategies automatically to produce a wide range of different descriptions to users who fall between the extremes of novice or expert, without requiring an a priori set of user stereotypes.

Building user interfaces by direct manipulation

Abstract: User interfaces based on mice, bitmap displays and windows are becoming commonplace, and there are guidelines on how such interfaces should function [Apple 85]. As a consequence, there is a growing expectation that all programs, no matter how trivial or how complicated, should present a graphically elegant and sophisticated user interface.

The berkeley UNIX consultant project

Abstract: UC (UNIX Consultant) is an intelligent, natural language interface that allows naive users to learn about the UNIX2 operating system. UC was undertaken because the task was thought to be both a fertile domain for artificial intelligence (AI) research and a useful application of AI work in planning, reasoning, natural language processing, and knowledge representation.The current implementation of UC comprises the following components: a language analyzer, called ALANA, produces a representation of the content contained in an utterance; an inference component, called a concretion mechanism, that further refines this content; a goal analyzer, PAGAN, that hypothesizes the plans and goals under which the user is operating; an agent, called UCEgo, that decides on UC's goals and proposes plans for them; a domain planner, called KIP, that computes a plan to address the user's request; an expression mechanism, UCExpress, that determines the content to be communicated to the user, and a language production mechanism, UCGen, that expresses UC's response in English.UC also contains a component, called KNOME, that builds a model of the user's knowledge state with respect to UNIX. Another mechanism, UCTeacher, allows a user to add knowledge of both English vocabulary and facts about UNIX to UC's knowledge base. This is done by interacting with the user in natural language.All these aspects of UC make use of knowledge represented in a knowledge representation system called KODIAK. KODIAK is a relation-oriented system that is intended to have wide representational range and a clear semantics, while maintaining a cognitive appeal. All of UC's knowledge, ranging from its most general concepts to the content of a particular utterance, is represented in KODIAK.

Modeling the user in natural language systems

Abstract: For intelligent interactive systems to communicate with humans in a natural manner, they must have knowledge about the system users. This paper explores the role of user modeling in such systems. It begins with a characterization of what a user model is and how it can be used. The types of information that a user model may be required to keep about a user are then identified and discussed. User models themselves can vary greatly depending on the requirements of the situation and the implementation, so several dimensions along which they can be classified are presented. Since acquiring the knowledge for a user model is a fundamental problem in user modeling, a section is devoted to this topic. Next, the benefits and costs of implementing a user modeling component for a system are weighed in light of several aspects of the interaction requirements that may be imposed by the system. Finally, the current state of research in user modeling is summarized, and future research topics that must be addressed in order to achieve powerful, general user modeling systems are assessed.

Modeling the user's plans and goals

Abstract: This work is an ongoing research effort aimed both at developing techniques for inferring and constructing a user model from an information-seeking dialog and at identifying strategies for applying this model to enhance robust communication. One of the most important components of a user model is a representation of the system's beliefs about the underlying task-related plan motivating an information-seeker's queries. These beliefs can be used to interpret subsequent utterances and produce useful responses. This paper describes the IREPS system, emphasizing its dynamic construction of the task-related plan motivating the information-seeker's queries and the application of this component of a user model to handling utterances that violate the pragmatic rules of the system's world model. By reasoning on a model of the user's plans and goals, the system often can deduce the intended meaning of faulty utterances and allow the dialogue to continue without interruption. Some limitations of current plan inference systems are discussed. It is suggested that the problem of detecting and recovering from discrepancies between the system's model of the user's plan and the actual plan under construction by the user requires an enriched model that differentiates among its components on the basis of the support the system accords each component as a correct and intended part of the user's plan.

A knowledge-based user interface management system

Abstract: A knowledge base which defines a user-computer interface is described. The knowledge base serves as input to a user interface management system, which implements the user interface. However, the knowledge base represents user interface design knowledge at a level of abstraction higher than is typical of user interface management systems. In particular, it represents objects, actions, attributes of objects, an object class hierarchy, and pre- and post-conditions on the actions. The knowledge base can be algorithmically transformed into a number of functionally equivalent interfaces, each of which is slightly different from the original interface. The transformed interface definition can be input to the UIMS, providing a way to quickly experiment with a family of related interfaces.

A Study of User Interface Aids for Model-Oriented Decision Support Systems

Abstract: The growing importance of DSS for both strategy evaluation and end user computing increases the need to provide research-based guidance for the design of user interface aids. This research study addresses the common belief that greater flexibility and choice in software aids will promote improved user performance. For model-based decision support systems, the effects of user vs. system-guided model manipulation, variable vs. exception-based report content, and display of incremental changes vs. actual outcomes on strategy formulation were investigated in a laboratory experiment with 46 undergraduate business students. The findings suggest that a system-guided or a more structured model manipulation strategy and the display of incremental changes significantly improve performance. Thus, for this task context, evidence is presented which runs counter to popular beliefs.

Interactive knowledge base end user interface driven maintenance and acquisition system

Abstract: A knowledge base system includes separate expert user and end user interface facilities which communicate with the system's inference engine and knowledge base. Linking means operatively interconnect the expert user facility to the end user interface facility enabling the simulation of the end user environment while the knowledge base is being concurrently edited or modified. Such simulation is carried out by placing the end user interface facility in an edit mode of operation during which the user can stop at any point. In response to a single keybgoard command, an expert user can switch control back to the expert user facility to a point within the knowledge base selected relative to the context of the operation which was being run thereby facilitating the maintenance and updating of the knowledge base.

The use of explicit user models in text generation: tailoring to a user's level of expertise

Abstract: A question answering program that provides access to a large amount of data will be most useful if it can tailor its answers to each individual user. In particular, a user's level of knowledge about the domain of discourse is an important factor in this tailoring if the answer provided is to be both informative and understandable to the user. In this research, we address the issue of how the user's domain knowledge, or the level of expertise, might affect an answer. By studying texts we found that the user's level of domain knowledge affected the kind of information provided and not just the amount of information, as was previously assumed. Depending on the user's assumed domain knowledge, a description of a complex physical object can be either parts-oriented or process-oriented. Thus the user's level of expertise in a domain can guide a system in choosing the appropriate facts from the knowledge base to include in an answer. We propose two distinct descriptive strategies that can be used to generate texts aimed at naive and expert users. Users are not necessarily truly expert or fully naive however, but can be anywhere along a knowledge spectrum whose extremes are naive and expert. In this work, we show how our generation system, TAILOR, can use information about a user's level of expertise to combine several discourse strategies in a single text, choosing the most appropriate at each point in the generation process, in order to generate texts for users anywhere along the knowledge spectrum. TAILOR's ability to combine discourse strategies based on a user model allows for the generation of a wider variety of texts and the most appropriate one for the user.

Reasoning on a highlighted user model to respond to misconceptions

Abstract: Responses to misconceptions given by human conversational partners very often contain information refuting possible reasoning which may have led to the misconceptions. Surprisingly there is a great deal of regularity in these responses across different domains of discourse. For instance, one reason a user might have given an object a property it does not have is that the user confused the object with another similar object. In correcting such a misconception, a human conversational partner is likely to point out this possible confusion.This work describes a method for generating responses like the one just described by reasoning on a highlighted model of the user to identify possible sources of the error. Through a transcript study a number of response strategies were abstracted. Each strategy was associated with a structural configuration of the user model. For example, the above mentioned strategy of pointing out a similar confused object is associated with a configuration of the user model that indicates the user believes there is an important similar object that has the property involved in the misconception. Upon finding that configuration in the highlighted user model, the system can respond with the associated strategy.Notice that the reasoning must be done on a highlighted user model since the perception of both an object's importance and its similarity with another object change with the perspective being taken on the domain. This paper investigates how domain perspective can be modeled to provide the needed highlighting and introduces a similarity metric that is sensitive to the highlighting provided by the domain perspective. Finally, the paper shows how the highlighting affects misconception responses.

The need for user models in generating expert system explanation

Abstract: An explanation facility is an important component of an expert system, but current systems for the most pa r t have neglected the importance of tailoring a system's explanations to the user. This paper explores the role of user modelling in generating expert system explanations, making the claim that individualized user models are essential to produce good explanations when the system users vary in their knowledge of the domain, or in their goals, plans, and preferences. To make this argument, a characterization of explanation, and good explanation is made, leading t o a presentation of how knowledge about the user affects the various aspects of a good explanation. Individualized user models are not only important, it is practical to obtain them. A method for acquiring a model of the user's beliefs implicitly by 'eavesdropping' on the interaction between user and system is presented, along with examples of how this information can be used to tailor an explanation.

Graphics and Natural Language as Components of Automatic Explanation

Abstract: An important component of adaptable interactive systems is the ability to model the system's users. Previous systems have relied on user models tailored to the particular needs of that system alone. This paper presents the notion of a general user model, and describes some of our research on building a general user modelling facility that could be used by a variety of applications. This work focuses on the representation, maintenance, and acquisition issues of modelling long-term beliefs of the user, and describes a general facility for accomplishing these tasks.

Applying a theory of graphical presentation to the graphic design of user interfaces

Abstract: The increasing availability of computers with high-quality graphics and fonts has created an opportunity and an obligation for user interface designers. The opportunity is that designers can use graphical techniques to design more effective user interfaces. The obligation is that they must become experts at the design of graphical user interfaces. Current user interface toolkits provide very little design assistance. This paper describes a theory that supports automatic design of graphical presentations of relational information and shows how to extend it to support theory-driven design of graphical user interfaces.“A picture worth a thousand words must first be a good picture” [Bow68]

Software environment architectures and user interface facilities

Abstract: The authors discuss the demands and constraints on a user interface management system for a software environment, and the relation between the architecture of the environment and the user interface management system. A model for designing user interface management systems for large extensible environments is presented. This model synthesizes several recent advances in user interfaces and specializes them to the domain of software environments. The model can be applied to a wide variety of environment contexts. A prototype implementation is described. >

Enhancing incremental learning processes with knowledge-based systems

Abstract: In the past computer systems limited the user to modes of communication that made the machine’s job easier. But now, as computer cycles become plentiful, our focus can shift to the users and how to make it easier, more productive, and less frustrating for them to cope with complex systems. Empirical investigations show that on the average only a small fraction of the functionality of complex systems is used. Figure 7.1 summarizes data based on careful observations of persons using systems like UNIX, EMACS, SCRIBE, LISP, and so on in our environment. It also describes different levels of system usage that typically can be found within many complex systems. The different domains correspond to the following: D1 the subset of concepts (and their associated commands) that the users know and use without any problems. D2 the subset of concepts that they use only occasionally. Users do not know details about them, and they are not too sure about their effects. Descriptions of commands (e.g., in the form of property sheets), explanations, illustrations (see the section on visualization techniques in prototypical system components) and safeguards (e.g., UNDOs) are important so that the user can gradually master this domain.

What we know and what we need to know: the user model versus the user's model in human-computer interaction

Abstract: An argument is made for the importance of studying the real, as opposed to idealized, behaviour of the computer user Formal methods which model user behaviour in terms of production rules are criticized because they fail to account for the unique behaviour which results either from problems arising in the normal work routine, or from novices who create their own patterns of interaction with the machine This latter point is illustrated with reference to a study of novice users How well are such users able to identify the knowledge they need when learning how to use a new system, and what kinds of knowledge of the system do they seek? It seems that in the absence of a suitable, generalizable model of a word processing system, these users structure their own learning experience badly, making poor use of the little experience they have The behaviour of such users deviates markedly from that of the ‘ideal user’ captured within formal task descriptions

A general user modelling facility

Abstract: An important component of adaptable interactive systems is the ability to model the system's users. Previous systems have relied on user models tailored to the particular needs of that system alone. This paper presents the notion of a general user model, and describes some of our research on building a general user modelling facility that could be used by a variety of applications. This work focuses on the representation, maintenance, and acquisition issues of modelling long-term beliefs of the user, and describes a general facility for accomplishing these tasks.

Focusing real-time systems analysis on user operations

Abstract: A way is presented to model and validate complex, real-time systems by describing these systems from the viewpoints of the major parties in system development: the customer, the user, and the implementer. The models representing these points of view are called, respectively, the requirements model, the operations-concept model, and the implementation model. The focus is on how the concept of operations can be formally factored into established real-time system-analysis methods in terms a nontechnical user can understand. The approach integrates and refines several real-time-oriented modeling methods, including structured analysis, finite-state machines, and threads. Modeling needs are discussed, the multiview paradigm is presented, and an extensive example of a bottle-filling system is given to illustrate the use of the method. >

IR-NLI II: applying man-machine interaction and artificial intelligence conceptsto information retrieval

Abstract: This paper addresses the problem of building expert interfaces to information retrieval systems. In particular, the problem of augmenting the capabilities of such interfaces with user modeling features is discussed and the main benefits of this approach are outlined. The paper presents a prototype system called IR-NLI II, devoted to model by means of artificial intelligence techniques the human intermediary to information retrieval systems. The overall organization of the IR-NLI II system is presented, together with a short description of the two main modules implemented so far, namely the Information Retrieval Expert Subsystem and the User Modeling Subsystem. An example of interaction with IR-NLI II is described. Perspectives and future research directions are finally outlined.

Retrieval based on user behaviour

Abstract: This paper gives an overview of the ongoing research in the Active Data Bases project at the Vrije Universiteit, Amsterdam. In this project we are specifying and building a system that helps a user in his search for useful and interesting information in large, complex information systems. The system is able to do this, because it learns from the interaction about the users and the data it contains. The indications of the users are expressed in terms of interests in the data, which serve as building blocks for user and data models. These models are then used to improve the search for interesting data.

Man-machine interfaces to medical information systems

Abstract: A joint project to provide an environment for prototyping and validating advanced man-machine interaction techniques for medical information systems is reported. The topics discussed are: data models and knowledge-representation languages for describing medical information, graphical display and interaction environments, user modeling capabilities, and personalization tools. The preliminary results of the project are sketched. >

User modeling at a dolphin language laboratory

Abstract: Two dolphins have been taught to comprehend artificial languages. Because the dolphins' and trainers' time is limited. Several intelligent programs have been developed to make more efficient use of resources. The programs, a computer simulation of dolphin learning as known to data, models of the skills of the human staff, and a front end processor, use user modeling to address and reduce the complexity of the problems for which they are designed. Valid individual differences in both dolphins and trainers are represented, and both time resources are used more efficiently. >

Planning for advising

Abstract: Effective advice depends on knowledge of the plans and goals of the person requiring help. Planning advice must be at a cognitively appropriate level for the user. HICCUPS, a dynamic planning system for a direct manipulation statistics program, is based on an ideal user model. Plans are generated from goals inferred from explicit goal statements from the user, knowledge about the statistics program, and the recent interactions with the interface. This exploitation of environmental information and inherent domain structure to restrict the amount of search and inferencing is a vital part of intelligent reasoning which is both fast and effective.

Retrieval systems for the information seeker: can the role of the intermediary be automated?

Abstract: The introduction of automated information retrieval (IR) systems was met with great enthusiasm and predictions that manual literature searching soon would be replaced. Three decades later, IR systems have not progressed to the stage where any but the dedicated few can operate them without a highly skilled human intermediary acting as interface between user and system. In the interim, we have learned that the retrieval process is extremely complex both in terms of understanding people and their communication and in terms of understanding scientific information and technical vocabulary. Experiments with new techniques suggest to many the possibility of eliminating the human intermediary, either in large part or altogether; others would argue that the retrieval problems are too complex to be resolved for more than highly restricted domains. The possibility of eliminating the human intermediary is of current research interest to the several disciplines that are represented on this panel.The discussion will revolve around one central question: Is our inability to get end-users to search primarily a problem of understanding people and their communication, or is it primarily a problem of understanding scientific information and technical vocabulary? Among the constituent issues the panel will address are these: Which are the most fundamental problems in constructing retrieval interfaces for the information seeker?How successful have been the attempts to build end-user oriented interfaces?Is a single, elegant solution to the problems of language handling, query negotiation, user modeling, and information retrieval possible?What techniques hold the most promise for automating the functions of the intermediary?What approaches are likely to be fruitless?Under what conditions is it possible and appropriate to automate some or all of the intermediary's skills and function?

Discourse models, dialog memories, and user models

Abstract: In this paper, we discuss some terminological issues related to the notions of discourse models, dialog memories, and user models. It is not our goal to show how discourse modeling and user modeling should actually interact in a cooperat ive system, but to show how the notions of discourse model, dialog memory, and user model can be defined and related in order to prevent misunderstandings and confusion. We argue that dialog memory may be subsumed under user model, as well as under discourse model, but that the three concepts should not be identified. Several separating criteria are discussed. We conclude that discourse modeling and user modeling are two lines of research that are orthogonal to each other.

Establishing the relationship between discourse models and user models

Abstract: Many current research efforts have focused on building cooperat ive systems that interact with the{r users in a natural language such as English. To be effective, these systems must be robust, their dialog must be coherent, and their responses must be helpful to the user. A user m o d e l (UM), which can be modified during the interaction to represent updated beliefs about the current user, is one mechanism that can contribute to a robust, coherent, and cooperat ive dialog. In general, when we as speakers describe certain situations, we try to communicate these situations to our listeners. As proposed by some researchers (Webber 1978, Kamp 1984), speakers do so by attempting to get their listeners to construct an appropriate model: a d i scourse mode l . A discourse model (DM) is viewed as containing representations of entities, along with their properties and relations they participate in. The key, then, in successful communication is for the speaker to transmit as much information about those entities, their properties and relations to the listener so as to achieve the goals of the current interaction. From the point of view of a system, a computational discourse model is used by the system to generate and/or interpret a discourse. This paper focuses on the relationship between DMs and UMs. It starts by describing what a DM is, and the role it plays in a coherent di~dog. It then describes what a UM is, and the role it plays in a cooperat ive dialog. I argue that the DM should be viewed as one part of the UM-tha t is, as one part of the system's model of the user. The examples of the natural language interactions are presented in the context of a natural language interface to an expert system that provides advice on cooking with chilies. (Part of the data was taken from the section "Cooking with Chilies" that appeared in Bon A p p e t i t magazine, December 1986. The expert system can provide information about the different varieties of chili peppers as well as descriptions of how to " tu rn down the hea t " of the chilies (make them less spicy), and how to cook with them without getting any kind of skin or eye irritations.) I justify this by showing how DMs can be viewed as part of UMs and how both models can affect each other. In other words, part of the UMs that systems have correspond to the DM, that is, a representat ion of what is talked about in a specific interaction. This piece, which changes with each discourse, affects the UM and varies from interaction to interaction.