5 research outputs found
Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System
Conversational systems typically focus on functional tasks such as scheduling
appointments or creating todo lists. Instead we design and evaluate SlugBot
(SB), one of 8 semifinalists in the 2018 AlexaPrize, whose goal is to support
casual open-domain social inter-action. This novel application requires both
broad topic coverage and engaging interactive skills. We developed a new
technical approach to meet this demanding situation by crowd-sourcing novel
content and introducing playful conversational strategies based on storytelling
and games. We collected over 10,000 conversations during August 2018 as part of
the Alexa Prize competition. We also conducted an in-lab follow-up qualitative
evaluation. Over-all users found SB moderately engaging; conversations averaged
3.6 minutes and involved 26 user turns. However, users reacted very differently
to different conversation subtypes. Storytelling and games were evaluated
positively; these were seen as entertaining with predictable interactive
structure. They also led users to impute personality and intelligence to SB. In
contrast, search and general Chit-Chat induced coverage problems; here users
found it hard to infer what topics SB could understand, with these
conversations seen as being too system-driven. Theoretical and design
implications suggest a move away from conversational systems that simply
provide factual information. Future systems should be designed to have their
own opinions with personal stories to share, and SB provides an example of how
we might achieve this.Comment: To appear in 1st International Conference on Conversational User
Interfaces (CUI 2019
Intelligent techniques for context-aware systems
Nowadays, with advances in communication technologies, researches are focused in the fields
of designing new devices with increasing capabilities, implanting software frameworks or middleware
to make these devices interoperable. Building better human interfaces is a challenging
task and the adoption of Artificial Intelligence (AI) techniques to the process help associating
semantic meaning to devices which makes possible the gesture recognition and voice
recognition.
This thesis is mainly concerned with the open problem in context-aware systems: the
evaluation of these systems in Ambient Intelligence (AmI) environments. With regard to this
issue, we argue that due to highly dynamic properties of the AmI environments, it should
exist a methodology for evaluating these systems taking into account the type of scenarios.
However in order to support with a solid ground for that discussion, some elements are to
be discussed as well. In particular, we:
• use a commercial platform that allows us to design and manage the contextual information
of context- aware systems by means of a context manager included in the
architecture;
• analyze the formal representation of this contextual information by means of a knowledge
based system (KBS);
• discuss the possible methodologies to be used for modelling knowledge in KBS and our
approach;
• give reasons why intelligent agents is a valid technique to be applied to systems in AmI
environments;
• propose a generic multi-agent system (MAS) architecture that can be applied to a
large class of envisaged AmI applications;
• propose a multimodal user interface and its integration with our MAS;
• propose an evaluation methodology for context-aware systems in AmI scenarios.
The formulation of the above mentioned elements became necessary as this thesis was
developed. The lack of an evaluation methodology for context-aware systems in AmI environments,
where so many issues to be covered, took us to the main objective of this thesis.
In this regard:
• we provide an updated and exhaustive state-of-the-art of this matter;
• examine the properties and characteristics of AmI scenarios;
• put forward an evaluation methodology and experimentally test our methodology in
AmI scenarios. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------La Inteligencia Ambiental y los entornos inteligentes hacen hincapié en una mayor facilidad
de uso, soporte de servicios más eficientes, el apoderamiento de los usuarios, y el apoyo
a las interacciones humanas. En esta visión, las personas estarán rodeadas de interfaces
inteligentes e intuitivas incrustados en objetos cotidianos que nos rodean y los sistemas
desarrollados para este ambiente deberán reconocer y responder a la presencia de individuos
de una manera invisible y transparente a ellos. Esta tesis se centra principalmente en el
problema abierto en los sistemas sensibles al contexto: la evaluación de estos sistemas en los
entornos de Inteligencia Ambiental. Con respecto a este tema, se argumenta que debido a las
propiedades altamente dinámica de los entornos de inteligencia ambiental, deberÃa existir una
metodologÃa para la evaluación de estos sistemas, teniendo en cuenta el tipo de escenarios.
Sin embargo, con el fin de apoyar con una base sólida para la discusión, algunos elementos
deben ser discutidos también. En particular, nosotros:
• Usamos una plataforma comercial que nos permite diseñar y gestionar la información
contextual de los sistemas sensibles al contexto a través de un gestor de contexto
incluido en la arquitectura;
• Analizamos la representación formal de esta información contextual a través de un
sistema basado en el conocimiento (SBC);
• Discutimos las posibles metodologÃas que se utilizarán para el modelado del conocimiento
en SBC y nuestra aproximación y propuesta;
• Discutimos las razones del por qué los agentes inteligentes son una técnica válida para
ser aplicada a los sistemas en entornos inteligencia ambiental;
• Proponemos un sistema multi-agente (SMA), con una arquitectura genérica que se
puede aplicar a una gran clase de aplicaciones de inteligencia ambiental;
• Proponemos una interfaz de usuario multimodales y su integración con nuestro SMA; • Proponemos una metodologÃa de evaluación de los sistemas sensibles al contexto en los escenarios de inteligencia ambiental.
La formulación de los elementos antes mencionados se hizo necesaria en la medida que
esta tesis se ha desarrollado. La falta de una metodologÃa de evaluación de los sistemas
sensibles al contexto en entornos de inteligencia ambiental, donde existen tantos temas a
tratar, nos llevó al objetivo principal de esta tesis. En este sentido, en esta tesis:
• Proporcionamos un estado del arte actualizado y exhaustivo de este asunto;
• Examinamos las propiedades y caracterÃsticas de los escenarios de inteligencia ambiental;
• Proponemos una metodologÃa de evaluación para este tipo de sistemas y experimentalmente
probamos nuestra metodologÃa en diversos escenarios de inteligencia ambiental
Evaluating Spoken Language Interaction
To study the spoken language interface in the context of a complex problem-solving task, a group of users were asked to perform a spreadsheet task, alternating voice and keyboard input. A total of 40 tasks were performed by each participant, the first thirty in a group (over several days), the remaining ones a month later. The voice spreadsheet program used in this study was extensively instrumented to provide detailed information about the components of the interaction. These data, as well as analysis of the participants's utterances and recognizer output, provide a fairly detailed picture of spoken language interaction. Although task completion by voice took longer than by keyboard, analysis shows that users would be able to perform the spreadsheet task faster by voice, if two key criteria could be met: recognition occurs in real-time, and the error rate is sufficiently low. This initial experience with a spoken language system also allows us to identify several metrics, beyond those traditionally associated with speech recognition, that can be used to characterize system performance