Mixed-initiative Personal Assistants

Specification and implementation of flexible human-computer dialogs is challenging because of the complexity involved in rendering the dialog responsive to a vast number of varied paths through which users might desire to complete the dialog. To address this problem, we developed a toolkit for modeling and implementing task-based, mixed-initiative dialogs based on metaphors from lambda calculus. Our toolkit can automatically operationalize a dialog that involves multiple prompts and/or sub-dialogs, given a high-level dialog specification of it. Our current research entails incorporating the use of natural language to make the flexibility in communicating user utterances commensurate with that in dialog completion paths

Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes

As systems grow in size and complexity so do their configuration possibilities. Users of modern systems are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. In this thesis, we propose a technique to select what information to elicit from the user so that the system can recommend the maximum number of personalized configuration items. Our method is based on constructing configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Processes (MDPs). Within the model, association rules are used to automatically infer configuration decisions based on knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. We conclude by reporting results of a case study in which this method is applied to the privacy configuration of Facebook

A survey of the use of crowdsourcing in software engineering

The term 'crowdsourcing' was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering

Designing a Chatbot Social Cue Configuration System

Social cues (e.g., gender, age) are important design features of chatbots. However, choosing a social cue design is challenging. Although much research has empirically investigated social cues, chatbot engineers have difficulties to access this knowledge. Descriptive knowledge is usually embedded in research articles and difficult to apply as prescriptive knowledge. To address this challenge, we propose a chatbot social cue configuration system that supports chatbot engineers to access descriptive knowledge in order to make justified social cue design decisions (i.e., grounded in empirical research). We derive two design principles that describe how to extract and transform descriptive knowledge into a prescriptive and machine-executable representation. In addition, we evaluate the prototypical instantiations in an exploratory focus group and at two practitioner symposia. Our research addresses a contemporary problem and contributes with a generalizable concept to support researchers as well as practitioners to leverage existing descriptive knowledge in the design of artifacts

Smart Buildings

This talk presents an efficient cyberphysical platform for the smart management of smart buildings http://www.deepint.net. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart building is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study at Salamanca - Ecocasa. This platform could enable smart building to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering

Chain-based recommendations

Recommender systems are discovery tools. Typically, they infer a user's preferences from her behaviour and make personalized suggestions. They are one response to the overwhelming choices that the Web affords its users. Recent studies have shown that a user of a recommender system is more likely to be satisfied by the recommendations if the system provides explanations that allow the user to understand their rationale, and if the system allows the user to provide feedback on the recommendations to improve the next round of recommendations so that they take account of the user's ephemeral needs. The goal of this dissertation is to introduce a new recommendation framework that offers a better user experience, while giving quality recommendations. It works on content-based principles and addresses both the issues identified in the previous paragraph, i.e.\ explanations and recommendation feedback. We instantiate our framework to produce two recommendation engines, each focusing on one of the themes: (i) the role of explanations in producing recommendations, and (ii) helping users to articulate their ephemeral needs. For the first theme, we show how to unify recommendation and explanation to a greater degree than has been achieved hitherto. This results in an approach that enables the system to find relevant recommendations with explanations that have a high degree of both fidelity and interpretability. For the second theme, we show how to allow users to steer the recommendation process using a conversational recommender system. Our approach allows the user to reveal her short-term preferences and have them taken into account by the system and thus assists her in making a good decision efficiently. Early work on conversational recommender systems considers the case where the candidate items have structured descriptions (e.g.\ sets of attribute-value pairs). Our new approach works in the case where items have unstructured descriptions (e.g.\ sets of genres or tags). For each of the two themes, we describe the problem settings, the state-of-the-art, our system design and our experiment design. We evaluate each system using both offline analyses as well as user trials in a movie recommendation domain. We find that the proposed systems provide relevant recommendations that also have a high degree of serendipity, low popularity-bias and high diversity