Inferences,suppositions and explanatory extensions in argument interpretation

We describe a probabilistic approach for the interpretation of user arguments that integrates three aspects of an interpretation: inferences, suppositions and explanatory extensions. Inferences fill in information that connects the propositions in a user’s argument, suppositions postulate new information that is likely believed by the user and is necessary to make sense of his or her argument, and explanatory extensions postulate information the user may have implicitly considered when constructing his or her argument. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation—a Bayesian network. Our evaluations show that suppositions and explanatory extensions are necessary components of interpretations, and that users consider appropriate the suppositions and explanatory extensions postulated by our system. This article integrates and extends research described in George et al., 2004; Zukerman et al., 2004; Zukerman and George, 2005; George et al., 2005. The research described in this article was conducted while Sarah George was employed at Monash University and was supported in part by the ARC Centre for Perceptive and Intelligent Machines in Complex Environments.     

Improving accessibility with user-tailored interfaces

The first stage in the design of a user interface is the quest for its ‘typical user’, an abstract generalization of each user of the application. However, in web systems and other scenarios where the application can be used by dozens of different kinds of users, the identification of this ‘typical user’ is quite difficult, if not impossible. Our proposal is to avoid the construction of interactive dialogs during the design stage, building them dynamically once the specific cognitive, perceptual and motor requirements of the current user are known: that is, during the execution stage. This is the approach used by GADEA, an intelligent user interface management system (UIMS) able to separate the functionality of an application from its interface in real time. The system adapts the components of the interface depending on the information stored in a user model which is continuously updated by a small army of data-gathering agents.     

Adaptation and user expertise modelling in AthosMail

This article describes the User Model component of AthosMail, a speech-based interactive e-mail application developed in the context of the EU project DUMAS. The focus is on the system’s adaptive capabilities and user expertise modelling, exemplified through the User Model parameters dealing with initiative and explicitness of the system responses. The purpose of the conducted research was to investigate how the users could interact with a system in a more natural way, and the two aspects that mainly influence the system’s interaction capabilities, and thus the naturalness of the dialogue as a whole, are considered to be the dialogue control and the amount of information provided to the user. The User Model produces recommendations of the system’s appropriate reaction depending on the user’s observed competence level, monitored and computed on the basis of the user’s interaction with the system. The article also discusses methods for the evaluation of adaptive user models and presents results from the AthosMail evaluation.The research was done while the author was affiliated with the University of Art and Design Helsinki as the scientific coordinator of the DUMAS project.     

A multi-agent based user context Bayesian neural network analysis system

The increasing user mobility demands placed upon IT services necessitates an environment that enables users to access optimal services at any time and in any place. This study presents research conducted to develop a system that is capable of analyzing user IT service patterns and tendencies and provides the necessary service resources by sharing each user’s context information. First, each user’s context information is gathered to provide the multi-agent software training data necessary to describe user operations in a hybrid peer-to-peer (P2P) structured communication environment. Next, the data collected about each user’s mobile device is analyzed through a Bayesian based neural network system to identify the user’s tendency and extract essential service information. This information provides a communication configuration allowing the user access to the best communication service between the user’s mobile device and the local server at any time and in any place, thereby enhancing the ubiquitous computing environment.     

A user-adaptive city guide system with an unobtrusive navigation interface

In this paper, we describe an intelligent location-aware city guide system, which adapts to each user’s preferences, and uses an intuitive “metal detector” interface for navigation. Our system analyzes each user’s past location data history to estimate individual preferences, and allows users to find shops that match their tastes in the same way a metal detector would be used to detect metal objects. The procedure with which the system picks out shops that match each user’s preferences includes a newly developed place learning algorithm, which can efficiently find frequented places, complete with their proper names (e.g. “The Ueno Royal Museum”). We have conducted a series of evaluation tests at a popular shopping district inside Tokyo, and the results validate the effectiveness of our overall approach.     

Modeling sequences of user actions for statistical goal recognition

User goals are of major importance for an interface agent because they serve as a context to define what the user’s focus of attention is at a given moment. The user’s goals should be detected as soon as possible, after observing few user actions, in order to provide the user with timely assistance. In this article, we describe an approach for modeling and recognizing user goals from observed sequences of user actions by using Variable Order Markov models combined with an exponential moving average (EMA) on the prediction probabilities. The validity of our approach has been tested using data collected from real users in the Unix domain. The results obtained show that an interface agent can achieve near 90% average accuracy and over 58% online accuracy in predicting the most probable user goal after each observed action, in a time linear to the number of goals being modeled. We also found that the use of an EMA allows a faster convergence in the actual user goal.     

Prediction of user navigation patterns by mining the temporal web usage evolution

Advances in the data mining technologies have enabled the intelligent Web abilities in various applications by utilizing the hidden user behavior patterns discovered from the Web logs. Intelligent methods for discovering and predicting user’s patterns is important in supporting intelligent Web applications like personalized services. Although numerous studies have been done on Web usage mining, few of them consider the temporal evolution characteristic in discovering web user’s patterns. In this paper, we propose a novel data mining algorithm named Temporal N-Gram (TN-Gram) for constructing prediction models of Web user navigation by considering the temporality property in Web usage evolution. Moreover, three kinds of new measures are proposed for evaluating the temporal evolution of navigation patterns under different time periods. Through experimental evaluation on both of real-life and simulated datasets, the proposed TN-Gram model is shown to outperform other approaches like N-gram modeling in terms of prediction precision, in particular when the web user’s navigating behavior changes significantly with temporal evolution.     

User diversity and design representation: Towards increased effectiveness in Design for All

This paper addresses user modelling for “Design for All” in a model-based approach to Human-Computer Interaction, paying particular attention to placing user models within organisational role- and task-related contexts. After reviewing a variety of user modelling approaches, and deriving requirements for user modelling related to Design for All, the paper proposes a role-driven individualised approach. Such an approach is based on a model-based representation schema and a unifying notation that keeps the user’s models and the contextual information transparent and consistent. Individualisation is achieved by coupling symbolic model specifications with neural networking on synchronisation links between symbolic representation elements. As a result, user modelling for Design for All is achieved not by stereotypical user properties and functional roles, but by accommodating the actual users’ behaviour. Published online: 18 May 2001     

Content-based image retrieval using fuzzy perceptual feedback

In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user’s fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user’s fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user’s feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.

Finding User’s Interest Blocks using Significant Implicit Evidence for Web Browsing on Small Screen Devices

Recent researches on improving the efficiency and user experience of Web browsing on handhelds are seeking to solve the problem by re-authoring Web pages or making adaptations and recommendations according to user preference. Their basis is a good understanding of the relationship between user behaviors and user preference. We propose a practical method to find user’s interest blocks by machine learning using the combination of significant implicit evidences, which is extracted from four aspects of user behaviors: display time, viewing information items, scrolling and link selection. We also develop a customized Web browser for small screen devices to collect user behaviors accurately. For evaluation, we conduct an on-line user study and make statistical analysis based on the dataset, which shows that most types of the suggested implicit evidences are significant, and viewing information items is the least indicative aspect of user behaviors. The dataset is then processed off-line to find user’s interest blocks using the proposed method. Experimental results demonstrate the effectiveness of finding user’s interest blocks by machine learning using the combination of significant implicit evidences. Further analysis reveals the great effect of users and moderate effect of Websites on the usefulness of significant implicit evidences.     

Rapid behavior adaptation for human-centered robots in a dynamic environment based on the integration of primitive confidences on multi-sensor elements

This article presents a method for tele-operated mobile robots to rapidly adapt to behavior policies. Since real-time adaptation requires frequent observations of sensors and the behavior of users, rapid policy adaptation cannot be achieved when significant data are not differentiated from insignificant data in every process cycle. Our method solves this problem by evaluating the significance of data for learning based on changes in the degree of confidence. A small change in the degree of confidence can be regarded as reflecting insignificant data for learning (that data can be discarded). Accordingly, the system can avoid having to store experience data too frequently, and the robot can adapt more rapidly to changes in the user’s policy. In this article, we confirm that by taking advantage of a significance evaluation not only of proposition of behavior, but also of each proposition of each piece of sensor-level data, a robot can rapidly adapt to a user’s policy. We discuss the results of two experiments in static and dynamic environments, in both of which the user switched policies between “avoid” and “approach.”     

User—robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy

This paper describes a hands-off socially assistive therapist robot designed to monitor, assist, encourage, and socially interact with post-stroke users engaged in rehabilitation exercises. We investigate the role of the robot’s personality in the hands-off therapy process, focusing on the relationship between the level of extroversion–introversion of the robot and the user. We also demonstrate a behavior adaptation system capable of adjusting its social interaction parameters (e.g., interaction distances/proxemics, speed, and vocal content) toward customized post-stroke rehabilitation therapy based on the user’s personality traits and task performance. Three validation experiment sets are described. The first maps the user’s extroversion–introversion personality dimension to a spectrum of robot therapy styles that range from challenging to nurturing. The second and the third experiments adjust the personality matching dynamically to adapt the robot’s therapy styles based on user personality and performance. The reported results provide first evidence for user preference for personality matching in the assistive domain and demonstrate how the socially assistive robot’s autonomous behavior adaptation to the user’s personality can result in improved human task performance. This work was supported by USC Women in Science and Engineering (WiSE) Program and the Okawa Foundation.     

Toward accessible self-service kiosks through intelligent user interfaces

Public self-service kiosks provide key services such as ticket sales, airport check-in and general information. Such kiosks must be universally designed to be used by society at large, irrespective of the individual users’ physical and cognitive abilities, level of education and familiarity with the system. The noble goal of universal accessibility is hard to achieve. This study reports experiences with a universally designed kiosk prototype based on a multimodal intelligent user interface that adapts to the user’s physical characteristics. The user interacts with the system via a tall rectangular touch-sensitive display where the interaction area is adjusted to fit the user’s height. A digital camera is used to measure the user’s approximate reading distance from the display such that the text size can be adjusted accordingly. The user’s touch target accuracy is measured, and the target sizes are increased for users with motor difficulties. A Byzantine visualization technique is employed to exploit unused and unreachable screen real estate to provide the user with additional visual cues. The techniques explored in this study have potential for most public self-service kiosks.     

Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System

It is important that systems that exhibit proactive behaviour do so in a way that does not surprise or frustrate the user. Consequently, it is desirable for such systems to be both personalised and designed in such a way as to enable the user to scrutinise her user model (part of which should hold the rules describing the behaviour of the system). This article describes on-going work to investigate the design of a prototype system that can learn a given user’s behaviour in an office environment in order to use the inferred rules to populate a user model and support appropriate proactive behaviour (e.g. turning on the user’s fan under appropriate conditions). We explore the tension between user control and proactive services and consider issues related to the design of appropriate transparency with a view to supporting user comprehensibility of system behaviour. To this end, our system enables the user to scrutinise and possibly over-ride the ‘IF-THEN’ rules held in her user model. The system infers these rules from the context history (effectively a data set generated using a variety of sensors) associated with the user by using a fuzzy-decision-tree-based algorithm that can provide a confidence level for each rule in the user model. The evolution of the system has been guided by feedback from a number of real-life users in a university department. A questionnaire study has yielded supplementary results concerning the extent to which the approach taken meets users’ expectations and requirements.     

Adaptation of robot’s perception of fuzzy linguistic information by evaluating vocal cues for controlling a robot manipulator

This article proposes a method for adapting a robot’s perception of fuzzy linguistic information by evaluating vocal cues. The robot’s perception of fuzzy linguistic information such as “very little” depends on the environmental arrangements and the user’s expectations. Therefore, the robot’s perception of the corresponding environment is modified by acquiring the user’s perception through vocal cues. Fuzzy linguistic information related to primitive movements is evaluated by a behavior evaluation network (BEN). A vocal cue evaluation system (VCES) is used to evaluate the vocal cues for modifying the BEN. The user’s satisfactory level for the robot’s movements and the user’s willingness to change the robot’s perception are identified based on a series of vocal cues to improve the adaptation process. A situation of cooperative rearrangement of the user’s working space is used to illustrate the proposed system by a PA-10 robot manipulator.     

Conversational Collaborative Recommendation – An Experimental Analysis

Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user’s (past) stored preferences. However, content-based recommender system research has begun to look towards more conversational models of recommendation, where the user is actively engaged in directing search at recommendation time. Such interactions can range from high-level dialogues with the user, possibly in natural language, to more simple interactions where the user is, for example, asked to indicate a preference for one of k suggested items. Importantly, the feedback attained from these interactions can help to differentiate between the user’s long-term stored preferences, and her current (short-term) requirements, which may be quite different. We argue that such interactions can also be beneficial to collaborative recommendation and provide experimental evidence to support this claim.     

On neuroscience-based informational technologies for authentic models of individual mind

Progress of neuroscience and information technologies tentatively makes possible actual implementation of old ideas to enable individual human being informational individuality with ability of autonomous existence (“life”) in computers. The idea has numerous reflections in science fiction and mass media communications. It seems, that just now computer technologies and understanding of brain mechanisms have attained a level, which makes this idea to be feasible and contemporary. The authentic model of an individual can consist of neurocomputer-based expert system, endowed with multi-media abilities. The system simulates its user, reproducing a kind of an Alter Ego for the user. The simulation ability of the system is being fed into it as a result of a thorough knowledge engineering process, which includes multiple psychological tests and psycho-physical measurements. The alter ego multi-media system is self-adjusted in a process of a permanent day to day dialog with its unique user. As a result it acquires real characters of user’s individuality. The user can authorize use of the system, adjusted to simulate user’s features to represent user in communication with other persons. In this case the system operates in an Alter Homo mode, enabling the user with communication abilities a good deal superior to human ones. The text was submitted by the authors in English.     

Intelligent System for Adapting to a User’s Characteristics

In this paper, we propose an intelligent system that adapts itself to a user’s characteristics or habits. The proposed intelligent system is composed of two Learning Vector Quantisation (LVQ) networks, commonly used in the fields of pattern recognition and signal processing. From the external condition of the plant, the first LVQ network learns to recognise the pattern of the sensed signal, and then aids the second LVQ to learn the user’s characteristics or habits so as to automatically produce the user’s favoured output. To verify the usefulness of the proposed method, we simulated and experimented with a variable illuminator. Both simulation and experimental results showed that the proposed intelligent system learns to automatically produce the illuminator output that the user most favours for the circumstances.     

Intelligent Assistance in Authoring Dynamically Generated Web Interfaces

Since its emergence in the early 1990s, the WWW has become not only an information system of unprecedented size, but a universal platform for the development of services and applications. However, most of the advances in web technologies are intended for professional developers, paying poor attention to end-users with no programming abilities but with explicit needs of creating and customizing web-based presentations. This provides a strong motivation for end-users to act as designers at some point, leading to an emerging role of new computing-related professionals to be considered. This paper is an effort to leverage such difficulties by providing intelligent mechanism to assist end-users in web-based authoring tasks. To carry out such a challenge, intelligent user-monitoring techniques are exploited to obtain high-level information that will be used to infer the user’s preferences and assist him throughout the interaction. Furthermore, we report on how iteration patterns can be applied to avoid repetitive tasks that are automatically carried out on behalf of the user. In order to bring off a feasible trade-off between expressivity and ease of use, a user experiment to obtain the user’s perception and evaluate the hit-rate of our system is also presented.