Multimodal Dialogue Management for Multiparty Interaction with Infants

We present dialogue management routines for a system to engage in multiparty agent-infant interaction. The ultimate purpose of this research is to help infants learn a visual sign language by engaging them in naturalistic and socially contingent conversations during an early-life critical period for language development (ages 6 to 12 months) as initiated by an artificial agent. As a first step, we focus on creating and maintaining agent-infant engagement that elicits appropriate and socially contingent responses from the baby. Our system includes two agents, a physical robot and an animated virtual human. The system's multimodal perception includes an eye-tracker (measures attention) and a thermal infrared imaging camera (measures patterns of emotional arousal). A dialogue policy is presented that selects individual actions and planned multiparty sequences based on perceptual inputs about the baby's internal changing states of emotional engagement. The present version of the system was evaluated in interaction with 8 babies. All babies demonstrated spontaneous and sustained engagement with the agents for several minutes, with patterns of conversationally relevant and socially contingent behaviors. We further performed a detailed case-study analysis with annotation of all agent and baby behaviors. Results show that the baby's behaviors were generally relevant to agent conversations and contained direct evidence for socially contingent responses by the baby to specific linguistic samples produced by the avatar. This work demonstrates the potential for language learning from agents in very young babies and has especially broad implications regarding the use of artificial agents with babies who have minimal language exposure in early life

Towards the Use of Dialog Systems to Facilitate Inclusive Education

Continuous advances in the development of information technologies have currently led to the possibility of accessing learning contents from anywhere, at anytime, and almost instantaneously. However, accessibility is not always the main objective in the design of educative applications, specifically to facilitate their adoption by disabled people. Different technologies have recently emerged to foster the accessibility of computers and new mobile devices, favoring a more natural communication between the student and the developed educative systems. This chapter describes innovative uses of multimodal dialog systems in education, with special emphasis in the advantages that they provide for creating inclusive applications and learning activities

Hacia una educación inclusiva y personalizada mediante el uso de los sistemas de diálogo multimodal

Los continuos avances en el desarrollo de tecnologías de la información han dado lugar actualmente a la posibilidad de acceder a los contenidos educativos desde cualquier lugar, en cualquier momento y de forma casi instantánea. Sin embargo, la accesibilidad no es siempre considerada como criterio principal en el diseño de aplicaciones educativas, especialmente para facilitar su utilización por parte de personas con discapacidad. Diferentes tecnologías han surgido recientemente para fomentar la accesibilidad a las nuevas tecnologías y dispositivos móviles, favoreciendo una comunicación más natural con los sistemas educativos. En este artículo se describe el uso innovador de los sistemas de diálogo multimodales en el campo de la educación, con un especial énfasis en la descripción de las ventajas que ofrecen para la creación de aplicaciones educativas inclusivas y adaptadas a la evolución de los estudiantes.Continuous advances in the development of information technologies have currently led to the possibility of accessing learning contents from anywhere, at anytime and almost instantaneously. However, accessibility is not always the main objective in the design of educative applications, specifically to facilitate their adoption by disabled people. Different technologies have recently emerged to foster the accessibility of computers and new mobile devices favouring a more natural communication between the student and the developed educative systems. This paper describes innovative uses of multimodal dialog systems in education, with special emphasis in the advantages that they provide for creating inclusive applications and adapted to the students specific evolution.Trabajo parcialmente financiado por los proyectos MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) y TRA2010-20225-C03-01.Publicad

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field