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UMUAI Tables of Contents: 01020304050607080910111213

User Modeling and User-Adapted Interaction 3

Editors:Alfred Kobsa
Standard No:ISSN 0924-1868 (print) EISSN 1573-1391 (online)
Links:link.springer.com | Table of Contents
  1. UMUAI 1993 Volume 3 Issue 1
  2. UMUAI 1993 Volume 3 Issue 2
  3. UMUAI 1993 Volume 3 Issue 3
  4. UMUAI 1994 Volume 3 Issue 4

UMUAI 1993 Volume 3 Issue 1

A user model neural network for a personal news service BIBAKFull-Text 1-25
  Andrew Jennings; Hideyuki Higuchi
User modelling has been widely applied to pedantic situations, where we are attempting to infer the user's knowledge. In teaching it is important to know that the user has mastered the elementary concepts before proceeding with the advanced topics. However, the application of user modelling to information retrieval demands a quite different type of user model. Here we construct a user model for browsing, where the user is uncertain of exactly which information he desires. This requires a more inexact and robust user model, that can quickly give guidance to the system. We propose a user model based on neural networks that can be constructed incrementally. Performance of the model shows some promise for this approach. We discuss the advantages and limitations of the approach and its implications for user modelling.
Keywords: Neural networks; information retrieval; browsing
Using structural descriptions of interfaces to automate the modelling of user cognition BIBAKFull-Text 27-64
  Jon May; Philip J. Barnard; Ann Blandford
One approach to user modelling (Barnard et al., 1988) involves building approximate descriptions of the cognitive activity underlying task performance in human-computer interactions. This approach does not aim to simulate exactly what is going on in the user's head, but to capture the salient features of their cognitive processing. The technique requires several sets of production rules. One set maps from a real-world description of an interface design to an internal theoretical description. Other rules elaborate the theoretical description, while further rules map from the theoretical description to properties of user behaviour. This paper is concerned primarily with the first type of rule, for mapping from interface descriptions to theoretical description of cognitive activity. Here we show how structural descriptions of interface designs can be used to model user tasks, visual interface objects and screen layouts. Included in our treatment are some indications of how properties of cognitive activity and their behavioural consequences can be inferred from such structural descriptions. An expert system implementation of the modelling technique has been developed, and its structure is described, together with some examples of its use in the evaluation of HCI design scenarios.
Keywords: Cognition; usability; interface; HCI; design; task structure; icons; screen layout; expert systems
Adaptive systems: A solution to usability problems BIBAKFull-Text 65-87
  David Benyon
Improving the usability of computer systems is perhaps the most important goal of human-computer interaction research. Current approaches to usability engineering tend to focus on simply improving the interface. An alternative is to build intelligence into the system. However, in order to do this a more comprehensive analysis is required and systems must be designed so that they can be made adaptive. This paper examines the implications for systems analysis, design and usability specification if adaptive systems are to be a realistic solution to usability problems.
Keywords: Usability; adaptive systems; analysis; adaptive system architecture
Model-based cognitive diagnosis BIBAKFull-Text 89-106
  John Self
This paper considers the problem of cognitive diagnosis as an instance of general diagnosis, as studied in artificial intelligence. Cognitive diagnosis is the process of inferring a cognitive state from observations of performance. It is thus a key component of any system which attempts to build a dynamic model of the user of that system. Many issues in cognitive diagnosis, previously discussed informally, are mapped onto formal techniques, with consequent increased clarity and rigour. But it is concluded that the 'general' theories for diagnosis must be broadened to fully encompass the problems of cognitive diagnosis.
Keywords: Student modelling; diagnosis; fault models; hierarchical abstraction; default reasoning

UMUAI 1993 Volume 3 Issue 2

Defining the semantics of extended genetic graphs BIBAKFull-Text 107-153
  L. Niem; B. J. Fugére; P. Rondeau
In the present work, the semantics of the Extended Genetic Graph (EGG) is defined in order to eliminate limitations inherent in these graphs in the modelling of an ideal Student Model. The semantics of extended genetic graphs can be defined at two representational levels: conceptual and transactional. First, the student's knowledge as represented by EGG nodes is specified explicitly at the conceptual level using the conceptual graphs (CGs) as a representation. Secondly, the criteria for the definition and use of learning processes such as analogy, generalization, refinement, component, and deviation/correction are specified at the transactional level. These criteria are then associated with the conditions of existence of different EGG links as they are implicitly assumed in the semantics of these graphs. Once the conditions of their creation are known, the semantics of EGG links can be represented explicitly by the use of CGs and Predicate Transition Networks (PrTNs). These representations are then used for detecting different types of EGG links.
   Conceptual graphs combined with PrTNs are able to describe the semantic structures equivalent to those contained implicitly in EGGs. However, the semantics of the combined graph which is based on the results of cognitive psychology, natural language processing, as well as logic, are richer than the semantics of the EGG. Furthermore, the operations provided by the conceptual graph theory combined with the constraint specifications as expressed by PrTNs allow the modification of the learner graph. Thus, our proposed representational framework provides the basis for the construction of a deep dynamical student model. An example from the Boolean Algebra domain demonstrates its feasibility.
Keywords: information processing; learning; knowledge representation; CAI; ICAI; AI
Consulting a user model to address a user's inferences during content planning BIBAKFull-Text 155-185
  Ingrid Zukerman; Richard Mcconachy
Most Natural Language Generation systems developed to date assume that a user will learn only what is explicitly stated in the discourse. This assumption leads to the generation of discourse that states explicitly all the information to be conveyed, and does not address further inferences from the discourse. In this paper, we describe a student model which provides a qualitative representation of a student's beliefs and inferences, and a content planning mechanism which consults this model in order to address the above problems. Our mechanism performs inferences in backward reasoning mode to generate discourse that conveys the intended information, and in forward reasoning mode to draw conclusions from the presented information. The forward inferences enable our mechanism to address possible incorrect inferences from the discourse, and to omit information that may be easily inferred from the discourse. In addition, our mechanism improves the conciseness of the generated discourse by omitting information known by the student. The domain of our implementation is the explanation of concepts in high school algebra.
Keywords: content planning; student beliefs; inferences; backward reasoning; forward reasoning

UMUAI 1993 Volume 3 Issue 3

Adaptive hypertext navigation based on user goals and context BIBAKFull-Text 193-220
  Craig Kaplan; Justine Fenwick; James Chen
Hypertext systems allow flexible access to topics of information, but this flexibility has disadvantages. Users often become lost or overwhelmed by choices. An adaptive hypertext system can overcome these disadvantages by recommending information to users based on their specific information needs and preferences. Simple associative matrices provide an effective way of capturing these user preferences. Because the matrices are easily updated, they support the kind of dynamic learning required in an adaptive system.
   HYPERFLEX, a prototype of an adaptive hypertext system that learns, is described. Informal studies with HYPERFLEX clarify the circumstances under which adaptive systems are likely to be useful, and suggest that HYPERFLEX can reduce time spent searching for information by up to 40%. Moreover, these benefits can be obtained with relatively little effort on the part of hypertext authors or users.
   The simple models underlying HYPERFLEX's performance may offer a general and useful alternative to more sophisticated modelling techniques. Conditions under which these models, and similar adaptation techniques, might be most useful are discussed.
Keywords: adaptive interface applications; hypertext; user models; human-computer interaction; associative matrices; intelligent information retrieval; relevance feedback
User modelling in interactive explanations BIBAKFull-Text 221-247
  Alison Cawsey
In this paper I consider how user modelling can be used to improve the provision of complex explanations, and discuss in detail the user modelling component of the EDGE explanation system. This allows a user model to be both updated and used in an explanatory dialogue with the user. The model is updated based on the interactions with the user, relationships between concepts and a reviseable expertise level. The model in turn influences the planning of the explanation, allowing a more understandable explanation to be generated. I argue that both user modelling and an "interactive" style of presentation are important for explanations to be acceptable and understandable, and that each reinforces the other.
Keywords: user-modelling; explanation; dialogue; tutorial system
The intelligent help system COMFOHELP BIBAKFull-Text 249-282
  Jürgen Krause; Eva Mittermaier
The paper is concerned with the question of whether and under what conditions active help systems with plan recognition components that have been developed in the environment of artificial intelligence research are able to prove their value in the real context of commercial application programs. The question is investigated using the development of the COMFOHELP intelligent help system as an example. COMFOHELP supports the COMFOTEX graphical text processing program and has been developed by the Linguistic Information Science Group at the University of Regensburg since 1988. The system recognizes erroneous and suboptimal plans pursued by the user by analyzing the dialog history and comparing them with the correct plan for achieving the user's goal.
   Section 2 discusses the research situation and elaborates on those problems which up to now prevented research concepts for plan recognition and intelligent help systems from being practically applied. Testing error situations empirically is a first prerequisite since potential erroneous plans can only be established in real-world tests. The second prerequisite is a special system architecture which counteracts the problem of ambiguities in plan recognition. Section 3 introduces a first still restricted prototype version of COMFOHELP whose efficiency was verified in a statistical hypothesis test. The users performing their text processing tasks with the support of COMFOHELP came off significantly better than members of a reference group working without the intelligent help. Section 4 shows that the proposed COMFOHELP system architecture is reconfirmed by the results of extensive empirical investigations (with more than 100 users) of erroneous plans when using a more complex version of COMFOTEX. The architecture still proves to be worthwhile even when functionality is increased by a factor of three to four.
Keywords: intelligent help system; plan recognition; user modeling; adaptive systems; artificial intelligence; practicality

UMUAI 1994 Volume 3 Issue 4

User-model-driven generation of instructions BIBAKFull-Text 289-319
  Gerhard Peter; Dietmar Rösner
There has been a great deal of research on specific issues of user modeling (e.g., generation of explanations (Paris 88), implicit knowledge acquisition (Kass, Finin 87), exploitation of user feedback to compensate for the unreliability of user models (Moore, Paris 92), but to our knowledge no work has been done on how to integrate this work into a single system. In TECHDOC-I we combine a number of ideas and work from various areas in a single system, add some unique features (e.g., application of a double-stereotype mechanism to plan representation), and apply them to a new domain (instructions for car maintenance). TECHDOC-I provides support to a user in the area of car maintenance. All maintenance activities are represented by plans, which consist of plan steps. The dialogue between user and system is based on these plans. A user model ensures that the system adapts the content of the output to the user. The commands and explanations are given in natural language that is generated by a (multilingual) text generator.
Keywords: Intelligent help; user modeling; text generation
Forming user models by understanding user feedback BIBAKFull-Text 321-358
  Alex Quilici
An intelligent advisory system should be able to provide explanatory responses that correct mistaken user beliefs. This task requires the ability to form a model of the user's relevant beliefs and to understand and address feedback from users who are not satisfied with its advice. This paper presents a method by which a detailed model of the user's relevant domain-specific, plan-oriented beliefs can gradually be formed by trying to understand user feedback in an on-going advisory dialog. In particular, we consider the problem of constructing an automated advisor capable of participating in a dialog discussing which UNIX command should be used to perform a particular task. We show how to construct a model of a UNIX user's beliefs about UNIX commands from several different classes of user feedback. Unlike other approaches to inferring user beliefs, our approach focuses on inferring only the small set of beliefs likely to be relevant in contributing to the user's misconception. And unlike other approaches to providing advice, we focus on the task of understanding the user's descriptions of perceived problems with that advice.
Keywords: advice-giving systems; dialog systems; misconception detection and repair; constructing user models; understanding user feedback; UNIX advising
Workshop on Adaptivity and User Modeling in Interactive Software Systems BIBFull-Text 359-367
  Alfred Kobsa; Wolfgang Pohl