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IJHCS Tables of Contents: 40414243444546474849505152535455565758

International Journal of Human-Computer Studies 48

Editors:B. R. Gaines
Publisher:Academic Press
Standard No:ISSN 0020-7373; TA 167 A1 I5
Links:Table of Contents
  1. IJHCS 1998 Volume 48 Issue 1
  2. IJHCS 1998 Volume 48 Issue 2
  3. IJHCS 1998 Volume 48 Issue 3
  4. IJHCS 1998 Volume 48 Issue 4
  5. IJHCS 1998 Volume 48 Issue 5
  6. IJHCS 1998 Volume 48 Issue 6

IJHCS 1998 Volume 48 Issue 1

Evolution and Learning in Multiagent Systems BIB 1-7
  Sandip Sen
The Potential for the Evolution of Co-Operation among Web Agents BIBA 9-29
  Cristina Bicchieri; Martha E. Pollack; Carlo Rovelli; Ioannis Tsamardinos
In building intelligent network agents, computer scientists may employ a variety of different design strategies, and their design decisions can have a significant effect on the ultimate nature of network interactions. Some agent designs are "co-operative", and populations of agents based on them would be able to interact smoothly, effectively utilizing network resources. In contrast, other agent designs can lead to ineffective and wasteful competition for network resources, resulting in massive bottlenecks and unacceptable access delays. We focus here on a particular design question, the multiple-access problem: if an agent seeking a piece of information knows of several sites that have, or might have, that information, how many queries should it issue, and when? We provide a formal analysis that demonstrates the viability of cooperative responses to this question under certain assumptions. We then discuss the limitations of this analysis and present the results of experiments done using a genetic-algorithms approach in which simulated network agents "evolve" co-operative strategies, under less restrictive assumptions than those made in the formal analysis.
Learning Cases to Resolve Conflicts and Improve Group Behavior BIBA 31-49
  Thomas Haynes; Sandip Sen
Groups of agents following fixed behavioral rules can be limited in performance and efficiency. Adaptability and flexibility are key components of intelligent behavior which allow agent groups to improve performance in a given domain using prior problem-solving experience. We motivate the utility of individual learning by group members in the context of overall group behavior. In particular, we propose a framework in which individual group members learn cases from problem-solving experiences to improve their model of other group members. We use a testbed problem from the Distributed Artificial Intelligence literature to show that simultaneous learning by group members can lead to significant improvement in group performance and efficiency over agent groups following static behavioral rules.
Learning Organizational Roles for Negotiated Search in a Multiagent System BIBA 51-67
  M. V. N. Prasad; Victor R. Lesser; Susan E. Lander
This paper presents studies in learning a form of organizational knowledge called organizational roles in a multi-agent agent system. It attempts to demonstrate the viability and utility of self-organization in an agent-based system involving complex interactions within the agent set. We present a multi-agent parametric design system called L-TEAM where a set of heterogeneous agents learn their organizational roles in negotiated search for mutually acceptable designs. We tested the system on a steam condenser design domain and empirically demonstrated its usefulness. L-TEAM produced better results than its non-learning predecessor, TEAM, which required elaborate knowledge engineering to hand-code organizational roles for its agent set. In addition, we discuss experiments with L-TEAM that highlight the importance of certain learning issues in multi-agent systems.
Using Limited Information to Enhance Group Stability BIBA 69-82
  Sandip Sen; Neeraj Arora; Shounak Roychowdhury
The performance of individual agents in a group depends critically on the quality of information available to them about local and global goals and resources. In general, it is assumed that the more accurate and comprehensive the available information, the better is the expected performance of the individual and the group. This conclusion can be challenged in a number of scenarios. We investigate the use of limited information by agents in choosing between one of several different options, and conclude that if agents are kept ignorant about, or they deliberately ignore, any number of options, the group can converge faster to a stable and optimal configuration. We present a probabilistic analysis that sheds light on the observed phenomenon of quicker system convergence with less global information. This analysis suggests a desirable adaptive behavior on the part of individual agents. Experiments with agents following these adaptive behavior exhibits faster convergence. We also demonstrate how a couple of coalition formation schemes can improve the rate of convergence. A variable coalition formation mechanism is found to be more effective than a static one.
Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer BIBA 83-104
  Peter Stone; Manuela Veloso
Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn lower-level skills, but they must also learn to work together and to adapt to the behaviors of different opponents. We are using a robotic soccer system to study these different types of multiagent learning: low-level skills, collaborative and adversarial. Here we describe in detail our experimental framework. We present a learned, robust, low-level behavior that is necessitated by the multiagent nature of the domain, viz. shooting a moving ball. We then discuss the issues that arise as we extend the learning scenario to require collaborative and adversarial learning.
Adaptive Agent Tracking in Real-World Multiagent Domains: A Preliminary Report BIBA 105-124
  Milind Tambe; Lewis Johnson; Wei-Min Shen
Intelligent interaction in multi-agent domains frequently requires an agent to track other agents' mental states: their current goals, beliefs and intentions. Accuracy in this agent-tracking task is critically dependent on the accuracy of the tracker's (tracking agent's) model of the trackee (tracked agent). Unfortunately, in real-world situations, model imperfections arise due to the tracker's resource and information constraints, as well as due to trackees' dynamic behavior modification. While such model imperfections are unavoidable, a tracker must nonetheless attempt to be adaptive in its agent tracking. This article identifies key issues in adaptive agent tracking and presents an approach called DEFT. At its core, DEFT is based on discrimination-based learning. The main idea is to identify the deficiency of a model based on tracking failures, and revise the model by using features that are critical in discriminating successful and failed tracking episodes. Because in real-world situations the set of candidate discriminating features is very large, DEFT relies on knowledge-based focusing to limit the discrimination to those features that it determines were relevant in successful tracking episodes -- with an autonomous explanation capability as a major source of this knowledge. This article reports on experiments with an implementation of key aspects of DEFT in a complex synthetic air-to-air combat domain.
Bayesian Learning in Negotiation BIBA 125-141
  Dajun Zeng; Katia Sycara
Negotiation has been extensively discussed in game-theoretic, economic and management science literatures for decades. Recent growing interest in autonomous interacting software agents and their potential application in areas such as electronic commerce has give increased importance to automated negotiation. Evidence both from theoretical analysis and from observations of human interactions suggests that if decision makers can somehow take into consideration what other agents are thinking and furthermore learn during their interactions how other agents behave, their payoff might increase. In this paper, we propose a sequential decision-making model of negotiation, called Bazaar. It provides an adaptive, multi-issue negotiation model capable of exhibiting a rich set of negotiation behaviors. Within the proposed negotiation framework, we model learning as a Bayesian belief update process. In this paper, we present both theoretical analysis and initial experimental results showing that learning is beneficial in the sequential negotiation model.

IJHCS 1998 Volume 48 Issue 2

Electronic Monitoring Systems: An Examination of Physiological Activity and Task Performance within a Simulated Keystroke Security and Electronic Performance Monitoring System BIBA 143-157
  Ron Henderson; Doug Mahar; Anthony Saliba; Frank Deane; Renee Napier
Electronic monitoring systems are becoming a prominent feature of the modern office. The aims of the present study were three-fold. First, to assess the effects electronic security monitoring systems (ESM) have on the user's physiological state. Second, the researches aimed to examine the effects explicit security challenges have on both user behaviour and physiological state when using an ESM system. Finally, the research aimed to examine the effects one form of electronic performance monitoring system may have on the user's physiological state. To this effect, the present study examined the physiological and performance effects of two simulated electronic monitoring systems (security/performance). The computer task required 32 subjects to enter mock clinical case notes under various conditions. In the first session subjects were only required to enter the case notes while keystroke data were collected. In the "security baseline" condition subjects were informed that a keystroke security monitoring system had been instituted, but no security challenges occurred. In the "security challenge" condition, however, a number of explicit security challenges occurred. In the final "performance monitoring" condition, subjects were informed that their data entry speed was monitored and they were placed on a response-cost schedule for poor performance. Blood pressure and continuous inter-heartbeat latency were recorded for the security and performance conditions. Results indicated that monitoring systems have the potential to evoke altered arousal states in the form of increased heart rate and blood pressure. Contrary to expectations, the hypothesized improvement in task performance within the performance monitoring condition was not observed. The implications of these results for the design and implementation of electronically based behavioural-based security and performance monitoring systems are discussed.
Impacts of Decision Task, Data and Display on Strategies for Extracting Information BIBA 159-180
  Miles Kennedy; Dov Te'Eni; James B. Treleaven
Decision tasks often require the extraction of information from displays of quantitative data. This paper investigates how people extract information from any one of several common displays by analysing the match between display, decision task and data. We posit two kinds of activity: first, the formulation of an appropriate extraction strategy and second, the execution of that strategy. We then develop a model of strategy formulation. We hypothesize that with matched designs a higher proportion of subjects use common strategies characterized by less time to formulate, less time to execute and more accurate decisions. A laboratory experiment using a new technique of graphical protocol analysis supported these hypotheses. Moreover, the experiment demonstrated how changes in display, decision task and data alter the way people select decision strategies. This suggests new opportunities for designing more effective human-computer interfaces.
The Essence of Problem-Solving Methods: Making Assumptions to Gain Efficiency BIBA 181-215
  Dieter Fensel; Remco Straatman
In this paper, we present the following view on problem-solving methods for knowledge-based systems: problem-solving methods describe an efficient reasoning strategy for achieving a goal by introducing assumptions about the available domain knowledge and the required functionality. Assumptions, dynamic reasoning behaviour and functionality are the three elements necessary to characterize a problem-solving method. In this paper, we elaborate this argument and introduce a framework for characterizing and developing such efficient problem solvers.
Lexical Accommodation in Human- and Machine-Interpreted Dialogues BIBA 217-246
  Laurel Fais
We report results of lexical accommodation studies involving three different interpretation settings: human-human monolingual; human-interpreted bilingual; and machine-interpreted bilingual. We found significant accommodation across all types of lexical items in all three conversational settings, with the highest rate in the human-interpreted setting. We also examine this phenomenon with respect to open-class part-of-speech categories. Motivations for accommodation, including speakers' concerns for social standing and communicational efficiency, are examined in the light of the results obtained for English, and briefly compared with quite different results obtained for Japanese. Finally, we draw implications for the design and implementation of multimedia human-computer interfaces.
Hands-Free Navigation in VR Environments by Tracking the Head BIBA 247-266
  Sing Bing Kang
Conventional methods of navigating within a virtual reality environment involve the use of interfaces such as keyboards, hand-held input devices such as joysticks, mice and trackballs, and hand-worn data gloves. While these devices are mostly adequate, they are rather obstrusive and require some amount of training to use. Researchers have begun investigation into interfaces that have the capability to interpret human gestures visually.
   In this document, we describe an approach used to navigate virtual reality environments by tracking the pose (translation and orientation) of the user's face. This "hands-free" navigation is simple, intuitive and unobstrusive. It requires only commercially available products such as a camera and an image digitizer. The pose of the face is determined by warping a reference face image to minimize intensity difference between the warped reference face image and the current face image. This is more robust because all pixels in the face are used, in contrast to detecting only selected facial features. In addition, the proposed approach does not require a geometric model of the face.
On Designing Comprehensible Interactive Hypermedia Manuals BIBA 267-301
  N. Hari Narayanan; Mary Hegarty
User's mental representations and cognitive strategies can have a profound influence on how they interact with computer interfaces (Janosky, Smith & Hildreth, 1986). However, there is very little research that elucidates such mental representations and strategies in the context of interactive hypermedia. Furthermore, interface design for hypermedia information presentation systems is rarely driven by what is known of users' mental models and strategies. This paper makes three contributions toward addressing these problems. First, it describes a novel cognitive model of comprehension of multimodal presentations for the specific application of explaining how machines work, and proposes guidelines for hypermedia design derived from this model. Since the development of this model draws heavily upon research in both cognitive science and computational modeling, a second contribution is that it contains a detailed review of literature in these fields on comprehension from static multimodal presentations. Third, it illustrates how cognitive and computational modeling are being used to inform the design of hypermedia information presentation systems about machines. This includes a framework for empirical validation of the model and evaluation of hypermedia design so that both theory and design can be refined iteratively.

IJHCS 1998 Volume 48 Issue 3

Introduction to the Special Issue "Using Context in Applications" BIB 303-305
  Patrick Brezillon
Context-Mediated Behavior for Intelligent Agents BIBA 307-330
  Roy M. Turner
Humans and other animals are exquisitely attuned to their context. Context affects almost all aspects of behavior, and it does so for the most part automatically, without a conscious reasoning effort. This would be a very useful property for an artificial agent to have: upon recognizing its context, the agent's behavior would automatically adjust to fit it. This paper describes context-mediated behavior (CMB), an approach to context-sensitive behavior we have developed over the past few years for intelligent autonomous agents. In CMB, contexts are represented explicitly as contextual schemas (c-schemas). An agent recognizes its context by finding the c-schemas that match it, then it merges these to form a coherent representation of the current context. This includes not only a description of the context, but also information about how to behave in it. From that point until the next context change, knowledge for context-sensitive behavior is available with no additional effort. This is used to influence perception, make predictions about the world, handle unanticipated events, determine the context-dependent meaning of concepts, focus attention and select actions. CMB is being implemented in the Orca program, an intelligent controller for autonomous underwater vehicles.
A Context Model for Knowledge-Intensive Case-Based Reasoning BIBA 331-355
  Pinar Ozturk; Agnar Aamodt
Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real-world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient or too brittle to handle new problems. In our research, we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem-solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem-solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained and an example shows its integration into an existing knowledge-intensive CBR system.
Contextual and Contextualized Knowledge: An Application in Subway Control BIBA 357-373
  P. Brezillon; J.-Ch. Pomerol; I. Saker
The control of the subway line traffic is a domain where operators must deal with huge quantities of pieces of knowledge more or less implicit in the control itself. When an incident occurs on a subway line, the operator must choose the best strategy applicable for moving from the incidental context to the operational one. An incident on the subway line may cause traffic delay or service interruption and may last for a long or short time, depending on the nature of the incident and many other elements. Operators mainly focus on contextual information for incident solving. An operator said, "When an incident occurs, I look first at what the incident context is". We propose to support subway line traffic operators in incident-solving with an incident-manager system, which is a part of the SART project (French acronym for support system in the traffic control). The incident manager is a decision-support system based on the contextual analysis of events that arise at the time of the incident. It uses a context-based representation of incidents and applies a context-based reasoning. In this paper we discuss a context-based representation of incidents on the basis of the onion metaphor. The SART project now enters the second year of the system design and development and implies two universities and two subway companies in France and Brazil.
Issues of Representing Context Illustrated by Video-Surveillance Applications BIBA 375-391
  Francois Bremond; Monique Thonnat
This paper tackles several issues of context representation in knowledge-based systems. First, we propose a definition of context through the description of the different types of information manipulated by a process. Thanks to this definition we explain the role of the granularity level of processing and the role of the abstraction level of application in modelling context. Based on this definition two main issues related to context are tackled: how context representation can be built and organized and how context contents can be re-used for other applications. Then we propose several solutions to deal with these issues: using a multi-viewpoint representation and describing context through symbolic information. We illustrate the proposed context model with the process of dynamic scene interpretation. After explaining the reasons why this process is particularly concerned with the use of contextual information, we describe the context representation and its implementation for this specific process. Finally, we give an example illustrating the utilization of the context representation and we describe the software we have developed to ease the acquisition stage of context contents.
A Distributed Fuzzy Constraint Satisfaction System with Context-Based Reasoning BIBA 393-407
  David A. Ress; Robert E. Young
This paper presents a fuzzy constraint satisfaction system which can be used in a distributive environment and, through an example, identifies contexts which exist within the constraint satisfaction system. The fuzzy constraint satisfaction system utilizes value propagation on constraints through the use of formal logic and theorem proving. The system has been designed to work in a distributive environment such that large problems can be broken down into smaller constraint networks for easier processing. Context-based reasoning is identified both within and among constraint networks. The paper begins with the motivation behind this research, followed by a description of the fuzzy constraint satisfaction system FuzCon. It concludes by identifying three mappings of the context-based reasoning ist operator to fuzzy constraints and by showing an example of designing a printed wiring board.

IJHCS 1998 Volume 48 Issue 4

Preface: Knowledge Acquisition for Planning BIB 409-416
  V. Richard Benjamins; Nigel Shadbolt
A Library of System-Derived Problem-Solving Methods for Planning BIBA 417-447
  Andre Valente; V. Richard Benjamins; Leliane Nunes De Barros
Constructing a planner for a particular application is a difficult job, for which little concrete support is currently available. The literature on planning is overwhelming and there exists no clear synthesis of the various planning methods which could be used by knowledge engineers. The contribution of this paper concerns an approach to provide concrete support for engineering planning systems. We use modern knowledge modeling approaches to analyse planning systems described in the literature. The analysis yields a detailed description of these planning systems in terms of the domain knowledge they use and the problem-solving methods they comprise. We show how the result of the analysis can be considered as a library of system-derived problem-solving methods for planning. This library consists of planning problem-solving methods along with their assumptions, which describe the applicability conditions on the domain knowledge of the methods. We describe how the library supports knowledge engineers in building planning systems and present two implemented tools based on the approach.
Knowledge Acquisition for Search and Rescue Planning BIBA 449-473
  Hugh Cottam; Nigel Shadbolt
There is an increasing adoption of knowledge-level modelling within expert system development. However, it has had less impact in the generic areas of planning, scheduling and resource allocation. In this paper, we outline the development of a knowledge-level modelling approach within the domain of planning for search and rescue (SAR). Existing problem solving models for planning are almost exclusively derived from an analysis of the functional architectures of classic AI planners such as TWEAK and NONLIN. We argue that this makes their suitability for directly assisting knowledge acquisition questionable. Our approach makes a clear distinction between domain-derived knowledge-level models and those derived from computational architectures. We describe how the combination of these two types of models can achieve clear benefits within the course of KBS development. The paper includes extensive descriptions of the SAR domain, which illustrate the practical knowledge engineering problems that our approach attempts to address.
Episodic Refinement of Episodic Skeletal-Plan Refinement BIBA 475-497
  S. W. Tu; M. A. Musen
This paper describes successive reformulations of skeletal-plan refinement as a problem-solving method. We argue that, whereas ideas derived from planning literature helped to determine the overall structure of the planning systems, domain-derived considerations and architectural framework in which the systems were implemented played important roles in these reformulations. We illustrate the argument by describing a new framework that integrates knowledge-based applications with a temporal data-abstraction and data-management system. In this framework, both applications and temporal-data mediators are encapsulated as Common Object Request Broker Architecture (CORBA) objects. The skeletal-plan refinement method itself is formulated as a collection of cooperating CORBA objects. We have found that we needed to reformulate the method ontology, mapping relations and control structure of the skeletal-planning problem-solving method in this framework. Our experience suggests that problem-solving methods are not necessarily fixed structures that can be plugged into arbitrary application environments, and that we need to develop a flexible configuration environment and expressive mapping formalisms to accommodate the requirements of application environments. These requirements include the ways data are made available and the ways software components interact with one another.
Static and Completion Analysis for Knowledge Acquisition, Validation and Maintenance of Planning Knowledge Bases BIBA 499-519
  Steve A. Chien
A key obstacle hampering the fielding of AI planning applications is the considerable expense of developing, verifying, updating and maintaining the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. Consequently, in order to field real systems, planning practitioners must be able to provide (1) tools to allow domain experts to create and debug their own planning knowledge bases; (2) tools for software verification, validation and testing and (3) tools to facilitate updates and maintenance of the planning knowledge base. This paper begins by describing a planning application of automated image processing and our overall approach to knowledge acquisition for this application. This paper then describes two types of tools for planning knowledge-base development: static KB analysis techniques to detect certain classes of syntactic errors in a planning knowledge-base and completion analysis techniques to interactively debug the planning knowledge base. We describe these knowledge development tools and describe empirical results documenting the usefulness of these tools.
Attention Allocation within the Abstraction Hierarchy BIBA 521-545
  Michael E. Janzen; Kim J. Vicente
Previous research has shown that Rasmussen's abstraction hierarchy, which consists of both physical and functional system models, provides a useful basis for interface design for complex human-machine systems. However, very few studies have quantitatively analysed how people allocate their attention across levels of abstraction. This experiment investigated the relationship between attention allocation strategies and performance on a thermal-hydraulic process simulation. Subjects controlled the process during both normal and fault situations for about an hour per weekday for approximately one month. All subjects used a multi-level interface consisting of four separate windows, each representing a level of the abstraction hierarchy. Subjects who made more frequent use of functional levels of information exhibited more accurate system control under normal conditions, and more accurate diagnosis performance under fault trials. Moreover, subjects who made efficient use of functional information exhibited faster fault compensation times. In contrast, subjects who made infrequent or inefficient use of functional information exhibited poorer performance on both normal and fault trials. These results provide some initial, specific evidence of the advantages of an abstraction hierarchy interface over more traditional interfaces that emphasize physical rather than functional information.

IJHCS 1998 Volume 48 Issue 5

Preface: Detecting, Repairing and Preventing Human -- Machine Miscommunication BIB 547-552
  Susan Mcroy
Conversational Adequacy: Mistakes Are the Essence BIBA 553-575
  Donald Perlis; Khemdut Purang; Carl Andersen
We argue that meta-dialog and meta-reasoning, far from being of only occasional use, are the very essence of conversation and communication between agents. We give four paradigm examples of massive use of meta-dialog where only limited object dialog may be present, and use these to bolster our claim of centrality for meta-dialog. We further illustrate this with related work in active logics. We argue moreover that there may be a core set of meta-dialog principles that is in some sense complete, and that may correspond to the human ability to engage in "free-ranging" conversation. If we are right, then implementing such a set would be of considerable interest. We give examples of existing computer programs that converse inadequately according to our guidelines.
Negative Feedback in Information Dialogues: Identification, Classification and Problem-Solving Procedures BIBA 577-604
  Barbara Derriks; Dominique Willems
This research -- essentially empirical and of a linguistic nature -- takes as its departure point the notion of feedback and the analysis of feedback phenomena in French informative dialogues. Our main concern has been to try to map the theoretical models of dialogue control with the empirical reality of dialogue problems. Supported by the statement of Bunt (1995a) that "understanding dialogue behaviour is central to a theory of dialogue", our research attempts to arrive at a better understanding of dialogue difficulties and reflects on how to construct an acceptable dialogue model adjusted in the best possible way to this complex reality. There is also a certain comparative perspective as we have been considering different kinds of dialogue data at different stages from human-human dialogues to human-machine dialogues.
   Our contribution will focus on three aspects: A proposal for the classification of 238 difficulties found in 117 man-man and 146 man-machine dialogues. An analysis of the linguistic "dialogue problem" devices found in the corpus as well as their interpretation. A tentative redefinition of feedback as a dynamic dialogue control mechanism which tries to recognize, correct and avoid dialogue problems.
A Methodology for Diagnostic Evaluation of Spoken Human -- Machine Dialogue BIBA 605-625
  Laila Dybkjaer; Niels Ole Bernsen; Hans Dybkjaer
Diagnostic evaluation is an important instrument for the development of high-quality spoken language dialogue systems. Yet no rigorous methodology exists for the systematic and exhaustive diagnostic evaluation of all aspects of spoken language interaction: recognition, synthesis, grammar, vocabulary, dialogue, etc. The paper addresses part of this problem by presenting a methodology for the diagnostic evaluation of spoken human-machine dialogue. The first part of the methodology is general and supports the detection of any kind of user-system interaction problem. The second part concerns the classification, diagnosis and repair of problems of dialogue interaction. These problems are of two broad kinds: dialogue design errors and user errors. Use of the methodology is demonstrated through analysis of a corpus from the user test of the Danish Dialogue System.
An Evaluation of Strategies for Selectively Verifying Utterance Meanings in Spoken Natural Language Dialog BIBA 627-647
  Ronnie W. Smith
As with human-human interaction, spoken human-computer dialog will contain situations where there is miscommunication. One natural strategy for reducing the impact of miscommunication is selective verification of the user utterance meanings. This paper reports on both context-independent and context-dependent strategies for utterance verification that show that the use of dialog context can be very helpful in selecting which utterances to verify. Simulations with data collected during experimental trials with the Circuit Fix-It Shop spoken natural language dialog system are used in the analysis. In addition, the performance of various selection strategies is measured separately for computer-controlled and user-controlled dialogs and general guidelines for selecting an appropriate strategy are presented.
A Plan-Based Model of Misunderstandings in Cooperative Dialogue BIBA 649-679
  Liliana Ardissono; Guido Boella; Rossana Damiano
We describe a plan-based agent architecture that models misunderstandings in cooperative NL agent communication; it exploits a notion of coherence in dialogue based on the idea that the explicit and implicit goals which can be identified by interpreting a conversational turn can be related with the previous explicit/implicit goals of the interactants. Misunderstandings are hypothesized when the coherence of the interaction is lost (i.e. an unrelated utterance comes). The processes of analysis (and treatment) of a misunderstanding are modelled as rational behaviours caused by the acquisition of a supplementary goal, when an incoherent turn comes: the agent detecting the incoherence commits to restore the intersubjectivity in the dialogue; so, he restructures his own contextual interpretation, or he induces the partner to restructure his (according to who seems to have made the mistake). This commitment leads him to produce a repair turn, which initiates a sub-dialogue aimed at restoring the common interpretation ground. Since we model speech acts uniformly with respect to the other actions (the domain-level actions), our model is general and covers misunderstandings occurring at the linguistic level as well as at the underlying domain activities of the interactants.
Achieving Robust Human-Computer Communication BIBA 681-704
  Susan W. Mcroy
This paper describes a computational approach to robust human-computer interaction. The approach relies on an explicit, declarative representation of the content and structure of the interaction that a computer system builds over the course of the interaction. In this paper, we will show how this representation allows the system to recognize and repair misunderstandings between the human and the computer. We demonstrate the utility of the representations by showing how they facilitate the repair process.

IJHCS 1998 Volume 48 Issue 6

Mental Representations of Spatial Language BIBA 705-728
  Geoffrey S. Hubona; Stephanie Everett; Elaine Marsh; Kenneth Wauchope
Previous studies have provided evidence of multi-level mental representations of language-conveyed spatial (scenic) information. However, the available evidence is largely inconclusive with regard to the structure of these mental representations. A laboratory experiment assesses computer-assisted problem-solving performance abilities when language-conveyed representations of spatial information are matched with the language perspective of the task and with individual cognitive skills. Our findings largely validate this paradigm of "cognitive fit" that has been applied in non-language computer display domains, and the results suggest language-fostered "perspective-bias" in the formation and use of mental representations of spatial (scenic) information.
A Longitudinal Study of the Effects of Ecological Interface Design on Deep Knowledge BIBA 729-762
  Klaus Christoffersen; Christopher N. Hunter; Kim J. Vicente
Some researchers have argued that providing operators with externalized, graphic representations can lead to a trade-off whereby deep knowledge is sacrificed for cognitive economy and performance. This article provides an initial empirical investigation of this hypothesis by presenting a longitudinal study of the effect of ecological interface design (EID), a framework for designing interfaces for complex industrial systems, on subjects' deep knowledge. The experiment continuously observed the quasi-daily performance of the subjects' over a period of six months. The research was conducted in the context of DURESS II, a real-time, interactive thermal-hydraulic process control simulation that was designed to be representative of industrial systems. The performance of two interfaces was compared, an EID interface based on physical and functional (P+F) system representations and a more traditional interface based solely on a physical (P) representation. Subjects were required to perform several control tasks, including startup, tuning, shutdown and fault management. Occasionally, a set of knowledge elicitation tests was administered to assess the evolution of subjects' deep knowledge of DURESS II. The results suggest that EID can lead to a functionally organized knowledge base as well as superior performance, but only if subjects actively reflect on the feedback they get from the interface. In contrast, if subjects adopt a surface approach to learning, then EID can lead to a shallow knowledge base and poor performance, although no worse than that observed with a traditional interface.
Replying to Email with Structured Responses BIBA 763-776
  Beatrice M. Camino; Allen E. Milewski; David R. Millen; Thomas M. Smith
Structured response objects include buttons, menus and formatted fields that an email sender can insert in a message to elicit predetermined responses from recipients. Two studies explored the usefulness of structured response objects in meeting the needs of everyday email. In Study 1, subjective content classifications suggested that more than half of typical email messages are requests or answers to requests. Further, a significant proportion of requests and answers could be expressed as structured response objects, the most common one being the choice of a single item from a predetermined list. Study 2 experimentally determined social factors that affect preference for structured responses compared with free form text. It found an overall preference for replying with structured responses compared with text. But, in accordance with social richness theories, this preference was reduced for ambiguous messages and for those of a personal nature. Together, these results suggest that structured response objects can be a useful tool to increase the convenience and efficiency of electronic messaging.
The Design and Evolution of Turboturtle, a Collaborative Microworld for Exploring Newtonian Physics BIBA 777-801
  Andy Cockburn; Saul Greenberg
TurboTurtle is a dynamic multi-user microworld for the exploration of Newtonian physics. With TurboTurtle, students can alter the attributes of the simulation environment, such as gravity, friction, and presence or absence of walls. Students explore the microworld by manipulating a variety of parameters, and learn concepts by studying the behaviours and interactions that occur. TurboTurtle has evolved into a "group-aware" system where several students, each on their own computer, can simultaneously control the microworld and gesture around the shared display. TurboTurtle's design rationale includes concepts such as equal opportunity controls, simulation timing, concrete vs. abstract controls, recoverability, and how strictly views should be shared between students. Teachers can also add structure to the group's activities by setting the simulation environment to an interesting state, which includes a set of problems and questions. Observations of pairs of young children using TurboTurtle highlight extremes in collaboration styles, from conflict to smooth interaction. Finally, the technical work in making TurboTurtle group-aware is slight, primarily because it was built with a groupware toolkit called GroupKit.