Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

A multimodal conversational coach for active ageing based on sentient computing and m-health

As Life Expectancy Increases, It Has Become More Necessary To Find Ways To Support Healthy Ageing. A Number Of Active Ageing Initiatives Are Being Developed Nowadays To Foster Healthy Habits In The Population. This Paper Presents Our Contribution To These Initiatives In The Form Of A Multimodal Conversational Coach That Acts As A Coach For Physical Activities. The Agent Can Be Developed As An Android App Running On Smartphones And Coupled With Cheap Widely Available Sport Sensors In Order To Provide Meaningful Coaching. It Can Be Employed To Prepare Exercise Sessions, Provide Feedback During The Sessions, And Discuss The Results After The Exercise. It Incorporates An Affective Component That Informs Dynamic User Models To Produce Adaptive Interaction Strategies.Spanish project, Grant/Award Number:TEC2017-88048-C2-2-R and TRA2016-78886-C3-1-

Digital Human Representations for Health Behavior Change: A Structured Literature Review

Organizations have increasingly begun using digital human representations (DHRs), such as avatars and embodied agents, to deliver health behavior change interventions (BCIs) that target modifiable risk factors in the smoking, nutrition, alcohol overconsumption, and physical inactivity (SNAP) domain. We conducted a structured literature review of 60 papers from the computing, health, and psychology literatures to investigate how DHRs’ social design affects whether BCIs succeed. Specifically, we analyzed how differences in social cues that DHRs use affect user psychology and how this can support or hinder different intervention functions. Building on established frameworks from the human-computer interaction and BCI literatures, we structure extant knowledge that can guide efforts to design future DHR-delivered BCIs. We conclude that we need more field studies to better understand the temporal dynamics and the mid-term and long-term effects of DHR social design on user perception and intervention outcomes

Critical Media Effects Framework: Bridging Critical Cultural Communication and Media Effects through Power, Intersectionality, Context, and Agency

In this essay, we advance the Critical Media Effects (CME) framework as a way of bridging two major subfields of communication that seldom speak to one another: media effects scholarship and critical cultural communication. Critical Media Effects is situated within the dominant mode of social scientific theorizing within media effects scholarship and draws on four key interrelated concepts from critical cultural communication: power, intersectionality, context, and agency. Critical Media Effects advocates for greater reflexivity, rigor, and nuance in theorizing about media effects to better respond to the complexity and dynamicity of emerging global sociopolitical mediated contexts. Recommendations, salient examples, and future directions for co-creating a shared research roadmap for CME are discussed. Through this work of bridging, we hope to promote more collaborative partnerships, productive engagement, and mutual solidarity across these two important subfields to address the most pressing social issues and challenges of the world today

Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching