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TIIS Tables of Contents: 01020304

ACM Transactions on Interactive Intelligent Systems 3

Editors:Anthony Jameson; John Riedl; Krzysztof Gajos
Dates:2013/2014
Volume:3
Publisher:ACM
Standard No:ISSN 2160-6455, EISSN 2160-6463
Papers:26
Links:Journal Home Page | ACM Digital Library | Table of Contents
  1. TIIS 2013-04 Volume 3 Issue 1
  2. TIIS 2013-07 Volume 3 Issue 2
  3. TIIS 2013-10 Volume 3 Issue 3
  4. TIIS 2014-01 Volume 3 Issue 4

TIIS 2013-04 Volume 3 Issue 1

Introduction to the special section on internet-scale human problem solving BIBAFull-Text 1
  Fausto Giunchiglia; David Robertson
This editorial introduction first outlines some of the research challenges raised by the emerging forms of internet-scale human problem solving. It then explains how the two articles in this special section can serve as illuminating complementary case studies, providing concrete examples embedded in general conceptual frameworks.
An internet-scale idea generation system BIBAFull-Text 2
  Lixiu Yu; Jeffrey V. Nickerson
A method of organizing the crowd to generate ideas is described. It integrates crowds using evolutionary algorithms. The method increases the creativity of ideas across generations, and it works better than greenfield idea generation. Specifically, a design space of internet-scale idea generation systems is defined, and one instance is tested: a crowd idea generation system that uses combination to improve previous designs. The key process of the system is the following: A crowd generates designs, then another crowd combines the designs of the previous crowd. In an experiment with 540 participants, the combined designs were compared to the initial designs and to the designs produced by a greenfield idea generation system. The results show that the sequential combination system produced more creative ideas in the last generation and outperformed the greenfield idea generation system. The design space of crowdsourced idea generation developed here may be used to instantiate systems that can be applied to a wide range of design problems. The work has both pragmatic and theoretical implications: New forms of coordination are now possible, and, using the crowd, it is possible to test existing and emerging theories of coordination and participatory design. Moreover, it may be possible for human designers, organized as a crowd, to codesign with each other and with automated algorithms.
Phrase detectives: Utilizing collective intelligence for internet-scale language resource creation BIBAFull-Text 3
  Massimo Poesio; Jon Chamberlain; Udo Kruschwitz; Livio Robaldo; Luca Ducceschi
We are witnessing a paradigm shift in Human Language Technology (HLT) that may well have an impact on the field comparable to the statistical revolution: acquiring large-scale resources by exploiting collective intelligence. An illustration of this new approach is Phrase Detectives, an interactive online game with a purpose for creating anaphorically annotated resources that makes use of a highly distributed population of contributors with different levels of expertise.
   The purpose of this article is to first of all give an overview of all aspects of Phrase Detectives, from the design of the game and the HLT methods we used to the results we have obtained so far. It furthermore summarizes the lessons that we have learned in developing this game which should help other researchers to design and implement similar games.
Interacting with social networks of intelligent things and people in the world of gastronomy BIBAFull-Text 4
  Luca Console; Fabrizio Antonelli; Giulia Biamino; Francesca Carmagnola; Federica Cena; Elisa Chiabrando; Vincenzo Cuciti; Matteo Demichelis; Franco Fassio; Fabrizio Franceschi; Roberto Furnari; Cristina Gena; Marina Geymonat; Piercarlo Grimaldi; Pierluige Grillo; Silvia Likavec; Ilaria Lombardi; Dario Mana; Alessandro Marcengo; Michele Mioli; Mario Mirabelli; Monica Perrero; Claudia Picardi; Federica Protti; Amon Rapp; Rossana Simeoni; Daniele Theseider Dupré; Ilaria Torre; Andrea Toso; Fabio Torta; Fabiana Vernero
This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure.
   The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.
picoTrans: An intelligent icon-driven interface for cross-lingual communication BIBAFull-Text 5
  Wei Song; Andrew Finch; Kumiko Tanaka-Ishii; Keiji Yasuda; Eiichiro Sumita
picoTrans is a prototype system that introduces a novel icon-based paradigm for cross-lingual communication on mobile devices. Our approach marries a machine translation system with the popular picture book. Users interact with picoTrans by pointing at pictures as if it were a picture book; the system generates natural language from these icons and the user is able to interact with the icon sequence to refine the meaning of the words that are generated. When users are satisfied that the sentence generated represents what they wish to express, they tap a translate button and picoTrans displays the translation. Structuring the process of communication in this way has many advantages. First, tapping icons is a very natural method of user input on mobile devices; typing is cumbersome and speech input errorful. Second, the sequence of icons which is annotated both with pictures and bilingually with words is meaningful to both users, and it opens up a second channel of communication between them that conveys the gist of what is being expressed. We performed a number of evaluations of picoTrans to determine: its coverage of a corpus of in-domain sentences; the input efficiency in terms of the number of key presses required relative to text entry; and users' overall impressions of using the system compared to using a picture book. Our results show that we are able to cover 74% of the expressions in our test corpus using a 2000-icon set; we believe that this icon set size is realistic for a mobile device. We also found that picoTrans requires fewer key presses than typing the input and that the system is able to predict the correct, intended natural language sentence from the icon sequence most of the time, making user interaction with the icon sequence often unnecessary. In the user evaluation, we found that in general users prefer using picoTrans and are able to communicate more rapidly and expressively. Furthermore, users had more confidence that they were able to communicate effectively using picoTrans.

TIIS 2013-07 Volume 3 Issue 2

Introduction to the special issue on interaction with smart objects BIBAFull-Text 6
  Daniel Schreiber; Kris Luyten; Max Mühlhäuser; Oliver Brdiczka; Melanie Hartman
Smart objects can be smart because of the information and communication technology that is added to human-made artifacts. It is not, however, the technology itself that makes them smart but rather the way in which the technology is integrated, and their smartness surfaces through how people are able to interact with these objects. Hence, the key challenge for making smart objects successful is to design usable and useful interactions with them. We list five features that can contribute to the smartness of an object, and we discuss how smart objects can help resolve the simplicity-featurism paradox. We conclude by introducing the three articles in this special issue, which dive into various aspects of smart object interaction: augmenting objects with projection, service-oriented interaction with smart objects via a mobile portal, and an analysis of input-output relations in interaction with tangible smart objects.
Cooperative augmentation of mobile smart objects with projected displays BIBAFull-Text 7
  David Molyneaux; Hans Gellersen; Joe Finney
Sensors, processors, and radios can be integrated invisibly into objects to make them smart and sensitive to user interaction, but feedback is often limited to beeps, blinks, or buzzes. We propose to redress this input-output imbalance by augmentation of smart objects with projected displays, that -- unlike physical displays -- allow seamless integration with the natural appearance of an object. In this article, we investigate how, in a ubiquitous computing world, smart objects can acquire and control a projection. We consider that projectors and cameras are ubiquitous in the environment, and we develop a novel conception and system that enables smart objects to spontaneously associate with projector-camera systems for cooperative augmentation. Projector-camera systems are conceived as generic, supporting standard computer vision methods for different appearance cues, and smart objects provide a model of their appearance for method selection at runtime, as well as sensor observations to constrain the visual detection process. Cooperative detection results in accurate location and pose of the object, which is then tracked for visual augmentation in response to display requests by the smart object. In this article, we define the conceptual framework underlying our approach; report on computer vision experiments that give original insight into natural appearance-based detection of everyday objects; show how object sensing can be used to increase speed and robustness of visual detection; describe and evaluate a fully implemented system; and describe two smart object applications to illustrate the system's cooperative augmentation process and the embodied interactions it enables with smart objects.
Embodying services into physical places: Toward the design of a mobile environment browser BIBAFull-Text 8
  Pierrick Thebault; Dominique Decotter; Mathieu Boussard; Monique Lu
The tremendous developments in mobile computing and handheld devices have allowed for an increasing usage of the resources of the World Wide Web. People today consume information and services on the go, through smart phones applications capable of exploiting their location in order to adapt the content according to the context of use. As location-based services gain traction and reveal their limitations, we argue there is a need for intelligent systems to be created to better support people's activities in their experience of the city, especially regarding their decision-making processes. In this article, we explore the opportunity to move closer to the realization of the ubiquitous computing vision by turning physical places into smart environments capable of cooperatively and autonomously collecting, processing, and transporting information about their characteristics (e.g., practical information, presence of people, and ambience). Following a multidisciplinary approach which leverages psychology, design, and computer science, we propose to investigate the potential of building communication and interaction spaces, called information spheres, on top of physical places such as businesses, homes, and institutions. We argue that, if the latter are exposed on the Web, they can act as a platform delivering information and services and mediating interactions with smart objects without requiring too much effort for the deployment of the architecture. After presenting the inherent challenges of our vision, we go through the protocol of two preliminary experiments that aim to evaluate users' perception of different types of information (i.e., reviews, check-in information, video streams, and real-time representations) and their influence on the decision-making process. Results of this study lead us to elaborate the design considerations that must be taken into account to ensure the intelligibility and user acceptance of information spheres. We finally describe a research prototype application called Environment Browser (Env-B) and present the underlying smart space middleware, before evaluating the user experience with our system through quantitative and qualitative methods.
An analysis of input-output relations in interaction with smart tangible objects BIBAFull-Text 9
  Evelien van de Garde-Perik; Serge Offermans; Koen van Boerdonk; Kars-Michiel Lenssen; Elise van den Hoven
This article focuses on the conceptual relation between the user's input and a system's output in interaction with smart tangible objects. Understanding this input-output relation (IO relation) is a prerequisite for the design of meaningful interaction. A meaningful IO relation allows the user to know what to do with a system to achieve a certain goal and to evaluate the outcome. The work discussed in this article followed a design research process in which four concepts were developed and prototyped. An evaluation was performed using these prototypes to investigate the effect of highly different IO relations on the user's understanding of the interaction. The evaluation revealed two types of IO relations differing in functionality and the number of mappings between the user and system actions. These two types of relations are described by two IO models that provide an overview of these mappings. Furthermore, they illustrate the role of the user and the influence of the system in the process of understanding the interaction. The analysis of the two types of IO models illustrates the value of understanding IO relations for the design of smart tangible objects.

Special section on eye gaze and conversation

Introduction to the special section on eye gaze and conversation BIBAFull-Text 10
  Elisabeth André; Joyce Chai
This editorial introduction first explains the origin of this special section. It then outlines how each of the two articles included sheds light on possibilities for conversational dialog systems to use eye gaze as a signal that reflects aspects of participation in the dialog: degree of engagement and turn taking behavior, respectively.
Gaze awareness in conversational agents: Estimating a user's conversational engagement from eye gaze BIBAFull-Text 11
  Ryo Ishii; Yukiko I. Nakano; Toyoaki Nishida
In face-to-face conversations, speakers are continuously checking whether the listener is engaged in the conversation, and they change their conversational strategy if the listener is not fully engaged. With the goal of building a conversational agent that can adaptively control conversations, in this study we analyze listener gaze behaviors and develop a method for estimating whether a listener is engaged in the conversation on the basis of these behaviors. First, we conduct a Wizard-of-Oz study to collect information on a user's gaze behaviors. We then investigate how conversational disengagement, as annotated by human judges, correlates with gaze transition, mutual gaze (eye contact) occurrence, gaze duration, and eye movement distance. On the basis of the results of these analyses, we identify useful information for estimating a user's disengagement and establish an engagement estimation method using a decision tree technique. The results of these analyses show that a model using the features of gaze transition, mutual gaze occurrence, gaze duration, and eye movement distance provides the best performance and can estimate the user's conversational engagement accurately. The estimation model is then implemented as a real-time disengagement judgment mechanism and incorporated into a multimodal dialog manager in an animated conversational agent. This agent is designed to estimate the user's conversational engagement and generate probing questions when the user is distracted from the conversation. Finally, we evaluate the engagement-sensitive agent and find that asking probing questions at the proper times has the expected effects on the user's verbal/nonverbal behaviors during communication with the agent. We also find that our agent system improves the user's impression of the agent in terms of its engagement awareness, behavior appropriateness, conversation smoothness, favorability, and intelligence.
Gaze and turn-taking behavior in casual conversational interactions BIBAFull-Text 12
  Kristiina Jokinen; Hirohisa Furukawa; Masafumi Nishida; Seiichi Yamamoto
Eye gaze is an important means for controlling interaction and coordinating the participants' turns smoothly. We have studied how eye gaze correlates with spoken interaction and especially focused on the combined effect of the speech signal and gazing to predict turn taking possibilities. It is well known that mutual gaze is important in the coordination of turn taking in two-party dialogs, and in this article, we investigate whether this fact also holds for three-party conversations. In group interactions, it may be that different features are used for managing turn taking than in two-party dialogs. We collected casual conversational data and used an eye tracker to systematically observe a participant's gaze in the interactions. By studying the combined effect of speech and gaze on turn taking, we aimed to answer our main questions: How well can eye gaze help in predicting turn taking? What is the role of eye gaze when the speaker holds the turn? Is the role of eye gaze as important in three-party dialogs as in two-party dialogue? We used Support Vector Machines (SVMs) to classify turn taking events with respect to speech and gaze features, so as to estimate how well the features signal a change of the speaker or a continuation of the same speaker. The results confirm the earlier hypothesis that eye gaze significantly helps in predicting the partner's turn taking activity, and we also get supporting evidence for our hypothesis that the speaker is a prominent coordinator of the interaction space. Such a turn taking model could be used in interactive applications to improve the system's conversational performance.

TIIS 2013-10 Volume 3 Issue 3

In Memoriam: John Riedl BIBAFull-Text 13
  Anthony Jameson
This recollection of John Riedl, founding coeditor-in-chief of the ACM Transactions on Interactive Intelligent Systems, presents a picture by editors of the journal of what it was like to collaborate and interact with him.
LiveAction: Automating Web Task Model Generation BIBAFull-Text 14
  Saleema Amershi; Jalal Mahmud; Jeffrey Nichols; Tessa Lau; German Attanasio Ruiz
Task automation systems promise to increase human productivity by assisting us with our mundane and difficult tasks. These systems often rely on people to (1) identify the tasks they want automated and (2) specify the procedural steps necessary to accomplish those tasks (i.e., to create task models). However, our interviews with users of a Web task automation system reveal that people find it difficult to identify tasks to automate and most do not even believe they perform repetitive tasks worthy of automation. Furthermore, even when automatable tasks are identified, the well-recognized difficulties of specifying task steps often prevent people from taking advantage of these automation systems.
   In this research, we analyze real Web usage data and find that people do in fact repeat behaviors on the Web and that automating these behaviors, regardless of their complexity, would reduce the overall number of actions people need to perform when completing their tasks, potentially saving time. Motivated by these findings, we developed LiveAction, a fully-automated approach to generating task models from Web usage data. LiveAction models can be used to populate the task model repositories required by many automation systems, helping us take advantage of automation in our everyday lives.
Characterizing and Predicting the Multifaceted Nature of Quality in Educational Web Resources BIBAFull-Text 15
  Philipp Wetzler; Steven Bethard; Heather Leary; Kirsten Butcher; Soheil Danesh Bahreini; Jin Zhao; James H. Martin; Tamara Sumner
Efficient learning from Web resources can depend on accurately assessing the quality of each resource. We present a methodology for developing computational models of quality that can assist users in assessing Web resources. The methodology consists of four steps: 1) a meta-analysis of previous studies to decompose quality into high-level dimensions and low-level indicators, 2) an expert study to identify the key low-level indicators of quality in the target domain, 3) human annotation to provide a collection of example resources where the presence or absence of quality indicators has been tagged, and 4) training of a machine learning model to predict quality indicators based on content and link features of Web resources. We find that quality is a multifaceted construct, with different aspects that may be important to different users at different times. We show that machine learning models can predict this multifaceted nature of quality, both in the context of aiding curators as they evaluate resources submitted to digital libraries, and in the context of aiding teachers as they develop online educational resources. Finally, we demonstrate how computational models of quality can be provided as a service, and embedded into applications such as Web search.
Plan Recognition and Visualization in Exploratory Learning Environments BIBAFull-Text 16
  Ofra Amir; Ya'akov (Kobi) Gal
Modern pedagogical software is open-ended and flexible, allowing students to solve problems through exploration and trial-and-error. Such exploratory settings provide for a rich educational environment for students, but they challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in such pedagogical software and visualizing these activities to teachers. It describes a new plan recognition algorithm that uses a recursive grammar that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using an exploratory environment for teaching chemistry used by thousands of students in several countries. It was always able to correctly infer students' plans when the appropriate grammar was available. We designed two methods for visualizing students' activities for teachers: one that visualizes students' inferred plans, and one that visualizes students' interactions over a timeline. Both of these visualization methods were preferred to and found more helpful than a baseline method which showed a movie of students' interactions. These results demonstrate the benefit of combining novel AI techniques and visualization methods for the purpose of designing collaborative systems that support students in their problem solving and teachers in their understanding of students' performance.
Human Decision Making and Recommender Systems BIBAFull-Text 17
  Li Chen; Marco de Gemmis; Alexander Felfernig; Pasquale Lops; Francesco Ricci; Giovanni Semeraro
Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.
An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising BIBAFull-Text 18
  Thomas Dodson; Nicholas Mattei; Joshua T. Guerin; Judy Goldsmith
A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected utility accrual over time. In this article, we present a novel explanation system for MDP policies. The system interactively generates conversational English-language explanations of the actions suggested by an optimal policy, and does so in real time. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations. Our explanation system is designed for portability between domains and uses a combination of domain-specific and domain-independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This MDP-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings used to generate English-language explanations.
Rating Bias and Preference Acquisition BIBAFull-Text 19
  Jill Freyne; Shlomo Berkovsky; Gregory Smith
Personalized systems and recommender systems exploit implicitly and explicitly provided user information to address the needs and requirements of those using their services. User preference information, often in the form of interaction logs and ratings data, is used to identify similar users, whose opinions are leveraged to inform recommendations or to filter information. In this work we explore a different dimension of information trends in user bias and reasoning learned from ratings provided by users to a recommender system. Our work examines the characteristics of a dataset of 100,000 user ratings on a corpus of recipes, which illustrates stable user bias towards certain features of the recipes (cuisine type, key ingredient, and complexity). We exploit this knowledge to design and evaluate a personalized rating acquisition tool based on active learning, which leverages user biases in order to obtain ratings bearing high-value information and to reduce prediction errors with new users.
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems BIBAFull-Text 20
  Bart P. Knijnenburg; Alfred Kobsa
Recommender systems increasingly use contextual and demographical data as a basis for recommendations. Users, however, often feel uncomfortable providing such information. In a privacy-minded design of recommenders, users are free to decide for themselves what data they want to disclose about themselves. But this decision is often complex and burdensome, because the consequences of disclosing personal information are uncertain or even unknown. Although a number of researchers have tried to analyze and facilitate such information disclosure decisions, their research results are fragmented, and they often do not hold up well across studies. This article describes a unified approach to privacy decision research that describes the cognitive processes involved in users' "privacy calculus" in terms of system-related perceptions and experiences that act as mediating factors to information disclosure. The approach is applied in an online experiment with 493 participants using a mock-up of a context-aware recommender system. Analyzing the results with a structural linear model, we demonstrate that personal privacy concerns and disclosure justification messages affect the perception of and experience with a system, which in turn drive information disclosure decisions. Overall, disclosure justification messages do not increase disclosure. Although they are perceived to be valuable, they decrease users' trust and satisfaction. Another result is that manipulating the order of the requests increases the disclosure of items requested early but decreases the disclosure of items requested later.

TIIS 2014-01 Volume 3 Issue 4

Integrated online localization and navigation for people with visual impairments using smart phones BIBAFull-Text 21
  Ilias Apostolopoulos; Navid Fallah; Eelke Folmer; Kostas E. Bekris
Indoor localization and navigation systems for individuals with Visual Impairments (VIs) typically rely upon extensive augmentation of the physical space, significant computational resources, or heavy and expensive sensors; thus, few systems have been implemented on a large scale. This work describes a system able to guide people with VIs through indoor environments using inexpensive sensors, such as accelerometers and compasses, which are available in portable devices like smart phones. The method takes advantage of feedback from the human user, who confirms the presence of landmarks, something that users with VIs already do when navigating in a building. The system calculates the user's location in real time and uses it to provide audio instructions on how to reach the desired destination. Initial early experiments suggested that the accuracy of the localization depends on the type of directions and the availability of an appropriate transition model for the user. A critical parameter for the transition model is the user's step length. Consequently, this work also investigates different schemes for automatically computing the user's step length and reducing the dependence of the approach on the definition of an accurate transition model. In this way, the direction provision method is able to use the localization estimate and adapt to failed executions of paths by the users. Experiments are presented that evaluate the accuracy of the overall integrated system, which is executed online on a smart phone. Both people with VIs and blindfolded sighted people participated in the experiments, which included paths along multiple floors that required the use of stairs and elevators.
Fluid gesture interaction design: Applications of continuous recognition for the design of modern gestural interfaces BIBAFull-Text 22
  Bruno Zamborlin; Frederic Bevilacqua; Marco Gillies; Mark D'Inverno
This article presents Gesture Interaction DEsigner (GIDE), an innovative application for gesture recognition. Instead of recognizing gestures only after they have been entirely completed, as happens in classic gesture recognition systems, GIDE exploits the full potential of gestural interaction by tracking gestures continuously and synchronously, allowing users to both control the target application moment to moment and also receive immediate and synchronous feedback about system recognition states. By this means, they quickly learn how to interact with the system in order to develop better performances. Furthermore, rather than learning the predefined gestures of others, GIDE allows users to design their own gestures, making interaction more natural and also allowing the applications to be tailored by users' specific needs. We describe our system that demonstrates these new qualities -- that combine to provide fluid gesture interaction design -- through evaluations with a range of performers and artists.
Design and evaluation techniques for authoring interactive and stylistic behaviors BIBAFull-Text 23
  James E. Young; Takeo Igarashi; Ehud Sharlin; Daisuke Sakamoto; Jeffrey Allen
We present a series of projects for end-user authoring of interactive robotic behaviors, with a particular focus on the style of those behaviors: we call this approach Style-by-Demonstration (SBD). We provide an overview introduction of three different SBD platforms: SBD for animated character interactive locomotion paths, SBD for interactive robot locomotion paths, and SBD for interactive robot dance. The primary contribution of this article is a detailed cross-project SBD analysis of the interaction designs and evaluation approaches employed, with the goal of providing general guidelines stemming from our experiences, for both developing and evaluating SBD systems. In addition, we provide the first full account of our Puppet Master SBD algorithm, with an explanation of how it evolved through the projects.
Triggering effective social support for online groups BIBAFull-Text 24
  Rohit Kumar; Carolyn P. Rosé
Conversational agent technology is an emerging paradigm for creating a social environment in online groups that is conducive to effective teamwork. Prior work has demonstrated advantages in terms of learning gains and satisfaction scores when groups learning together online have been supported by conversational agents that employ Balesian social strategies. This prior work raises two important questions that are addressed in this article. The first question is one of generality. Specifically, are the positive effects of the designed support specific to learning contexts? Or are they in evidence in other collaborative task domains as well? We present a study conducted within a collaborative decision-making task where we see that the positive effects of the Balesian social strategies extend to this new context. The second question is whether it is possible to increase the effectiveness of the Balesian social strategies by increasing the context sensitivity with which the social strategies are triggered. To this end, we present technical work that increases the sensitivity of the triggering. Next, we present a user study that demonstrates an improvement in performance of the support agent with the new, more sensitive triggering policy over the baseline approach from prior work.
   The technical contribution of this article is that we extend prior work where such support agents were modeled using a composition of conversational behaviors integrated within an event-driven framework. Within the present approach, conversation is orchestrated through context-sensitive triggering of the composed behaviors. The core effort involved in applying this approach involves building a set of triggering policies that achieve this orchestration in a time-sensitive and coherent manner. In line with recent developments in data-driven approaches for building dialog systems, we present a novel technique for learning behavior-specific triggering policies, deploying it as part of our efforts to improve a socially capable conversational tutor agent that supports collaborative learning.
Task model-driven realization of interactive application functionality through services BIBAFull-Text 25
  K. Kritikos; D. Plexousakis; F. Paternò
The Service-Oriented Computing (SOC) paradigm is currently being adopted by many developers, as it promises the construction of applications through reuse of existing Web Services (WSs). However, current SOC tools produce applications that interact with users in a limited way. This limitation is overcome by model-based Human-Computer Interaction (HCI) approaches that support the development of applications whose functionality is realized with WSs and whose User Interface (UI) is adapted to the user's context. Typically, such approaches do not consider various functional issues, such as the applications' semantics and their syntactic robustness in terms of the WSs selected to implement their functionality and the automation of the service discovery and selection processes. To this end, we propose a model-driven design method for interactive service-based applications that is able to consider the functional issues and their implications for the UI. This method is realized by a semiautomatic environment that can be integrated into current model-based HCI tools to complete the development of interactive service front-ends. The proposed method takes as input an HCI task model, which includes the user's view of the interactive system, and produces a concrete service model that describes how existing services can be combined to realize the application's functionality. To achieve its goal, our method first transforms system tasks into semantic service queries by mapping the task objects onto domain ontology concepts; then it sends each resulting query to a semantic service engine so as to discover the corresponding services. In the end, only one service from those associated with a system task is selected, through the execution of a novel service concretization algorithm that ensures message compatibility between the selected services.
Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging BIBAFull-Text 26
  Dimitrios Rafailidis; Apostolos Axenopoulos; Jonas Etzold; Stavroula Manolopoulou; Petros Daras
In this article, a novel method for personalized item recommendation based on social tagging is presented. The proposed approach comprises a content-based tag propagation method to address the sparsity and "cold start" problems, which often occur in social tagging systems and decrease the quality of recommendations. The proposed method exploits (a) the content of items and (b) users' tag assignments through a relevance feedback mechanism in order to automatically identify the optimal number of content-based and conceptually similar items. The relevance degrees between users, tags, and conceptually similar items are calculated in order to ensure accurate tag propagation and consequently to address the issue of "learning tag relevance." Moreover, the ternary relation among users, tags, and items is preserved by performing tag propagation in the form of triplets based on users' personal preferences and "cold start" degree. The latent associations among users, tags, and items are revealed based on a tensor factorization model in order to build personalized item recommendations. In our experiments with real-world social data, we show the superiority of the proposed approach over other state-of-the-art methods, since several problems in social tagging systems are successfully tackled. Finally, we present the recommendation methodology in the multimodal engine of I-SEARCH, where users' interaction capabilities are demonstrated.