Using Conceptual Models to Implement Natural Language Pedagogic Agent-Student Conversations

2013 ◽  
Vol 3 (2) ◽  
pp. 29-47 ◽  
Author(s):  
Diana Pérez-Marín ◽  
Carlos Caballero
Keyword(s):  
Natural Language ◽  
Conceptual Models ◽  
Interactive Learning Environments ◽  
Software Systems ◽  
Virtual Agents ◽  
Learning Needs ◽  
Conversational Agents ◽  
Agent Based ◽  
Intelligent Virtual Agents ◽  
Mixed Initiative

The last several decades have seen a growing trend in incorporating pedagogic conversational agents in interactive learning environments. Software systems have increasingly integrated intelligent virtual agents that can interact with students in natural language to fulfill specific tasks such as reviewing content or providing tutor training. The use of an agent-based approach in education has shown many benefits. However, certain design and development issues are still unresolved. This article focuses on the potentials of employing conceptual models to generate agent-student dialog and introduces a new mixed-initiative general domain agent called JARO. The authors report on the procedure for creating the initial conceptual model and discuss its use in guiding agent-student conversations adapted to students' individual learning needs. The stages of implementation of the model as well as the model's viability tested in a proof-of-concept experiment are addressed.

Virtual Reality in Object Location

2021 ◽  
pp. 307-324
Author(s):  
Graciela Lara López
Keyword(s):  
Virtual Reality ◽  
Natural Language ◽  
Computational Model ◽  
Everyday Life ◽  
Virtual Environments ◽  
Computer Technology ◽  
Object Location ◽  
Virtual Agents ◽  
Intelligent Virtual Agents

Currently, virtual reality (VR) is a computer technology that is growing in terms of developments and discoveries. Virtual reality has been introduced in different areas due to the growing interest it has caused in people. The development of applications with virtual reality is increasingly varied, covering activities, tasks, or processes of everyday life in the fields of industry, education, medicine, tourism, art, entertainment, design, and modeling of objects, among others. This chapter will focus on describing the latest advances and developments in virtual reality within the scope of representing reality in the process of locating objects. With the support of virtual environments and intelligent virtual agents, the author has managed to develop a computational model that generates indications in natural language, for the location of objects considering spatial and cognitive aspects of the users.

Remembering a Conversation – A Conversational Memory Architecture for Embodied Conversational Agents

2017 ◽  
Vol 26 (1) ◽  
pp. 1-21
Author(s):  
Miguel Elvir ◽  
Avelino J. Gonzalez ◽  
Christopher Walls ◽  
Bryan Wilder
Keyword(s):  
Episodic Memory ◽  
Natural Language ◽  
Virtual Agents ◽  
Embodied Conversational Agents ◽  
Memory Architecture ◽  
Conversational Agents ◽  
Memory Architectures

AbstractThis paper addresses the role of conversational memory in Embodied Conversational Agents (ECAs). It describes an investigation into developing such a memory architecture and integrating it into an ECA. ECAs are virtual agents whose purpose is to engage in conversations with human users, typically through natural language speech. While several works in the literature seek to produce viable ECA dialog architectures, only a few authors have addressed the episodic memory architectures in conversational agents and their role in enhancing their intelligence. In this work, we propose, implement, and test a unified episodic memory architecture for ECAs. We describe a process that determines the prevalent contexts in the conversations obtained from the interactions. The process presented demonstrates the use of multiple techniques to extract and store relevant snippets from long conversations, most of whose contents are unremarkable and need not be remembered. The mechanisms used to store, retrieve, and recall episodes from previous conversations are presented and discussed. Finally, we test our episodic memory architecture to assess its effectiveness. The results indicate moderate success in some aspects of the memory-enhanced ECAs, as well as some work still to be done in other aspects.

scholarly journals Comparison of disclosure/concealment of medical information given to conversational agents or to physicians

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822199486
Author(s):  
Nicholas RJ Frick ◽  
Felix Brünker ◽  
Björn Ross ◽  
Stefan Stieglitz
Keyword(s):  
Natural Language ◽  
Medical Information ◽  
Frame Of Reference ◽  
Future Research ◽  
Negative Consequences ◽  
Conversational Agents ◽  
Online Questionnaire ◽  
Computer Based

Within the anamnesis, medical information is frequently withheld, incomplete, or incorrect, potentially causing negative consequences for the patient. The use of conversational agents (CAs), computer-based systems using natural language to interact with humans, may mitigate this problem. The present research examines whether CAs differ from physicians in their ability to elicit truthful disclosure and discourage concealment of medical information. We conducted an online questionnaire with German participants ( N = 148) to assess their willingness to reveal medical information. The results indicate that patients would rather disclose medical information to a physician than to a CA; there was no difference in the tendency to conceal information. This research offers a frame of reference for future research on applying CAs during the anamnesis to support physicians. From a practical view, physicians might gain better understanding of how the use of CAs can facilitate the anamnesis.

scholarly journals Special issue on Current trends in research on software agents and agent-based software systems

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Matteo Baldoni ◽  
Federico Bergenti ◽  
Amal El Fallah Seghrouchni ◽  
Michael Winikoff
Keyword(s):  
Software Agents ◽  
Software Systems ◽  
Special Issue ◽  
Agent Based ◽  
Current Trends

A Transparent and Decentralized Model of Perception and Action for Intelligent Virtual Agents

2014 ◽  
Vol 23 (04) ◽  
pp. 1460020 ◽  
Author(s):  
George Anastassakis ◽  
Themis Panayiotopoulos
Keyword(s):  
Knowledge Representation ◽  
Virtual Environment ◽  
Virtual World ◽  
Perception And Action ◽  
Virtual Agents ◽  
Virtual Agent ◽  
Complex Interactions ◽  
Intelligent Virtual Agents ◽  
Decentralized Model ◽  
Over Time

Intelligent virtual agent behaviour is a crucial element of any virtual environment application as it essentially brings the environment to life, introduces believability and realism and enables complex interactions and evolution over time. However, the development of mechanisms for virtual agent perception and action is neither a trivial nor a straight-forward task. In this paper we present a model of perception and action for intelligent virtual agents that meets specific requirements and can as such be systematically implemented, can seamlessly and transparently integrate with knowledge representation and intelligent reasoning mechanisms, is highly independent of virtual world implementation specifics, and enables virtual agent portability and reuse.

scholarly journals Human Annotated Dialogues Dataset for Natural Conversational Agents

2020 ◽  
Vol 10 (3) ◽  
pp. 762
Author(s):  
Erinc Merdivan ◽  
Deepika Singh ◽  
Sten Hanke ◽  
Johannes Kropf ◽  
Andreas Holzinger ◽  
...  
Keyword(s):  
Natural Language ◽  
Detailed Analysis ◽  
Human Perception ◽  
Natural Language Understanding ◽  
Industrial Applications ◽  
Benchmark Dataset ◽  
Conversational Agents ◽  
High Quality ◽  
Language Understanding ◽  
Major Drawback

Conversational agents are gaining huge popularity in industrial applications such as digital assistants, chatbots, and particularly systems for natural language understanding (NLU). However, a major drawback is the unavailability of a common metric to evaluate the replies against human judgement for conversational agents. In this paper, we develop a benchmark dataset with human annotations and diverse replies that can be used to develop such metric for conversational agents. The paper introduces a high-quality human annotated movie dialogue dataset, HUMOD, that is developed from the Cornell movie dialogues dataset. This new dataset comprises 28,500 human responses from 9500 multi-turn dialogue history-reply pairs. Human responses include: (i) ratings of the dialogue reply in relevance to the dialogue history; and (ii) unique dialogue replies for each dialogue history from the users. Such unique dialogue replies enable researchers in evaluating their models against six unique human responses for each given history. Detailed analysis on how dialogues are structured and human perception on dialogue score in comparison with existing models are also presented.

scholarly journals Perceptions on Authenticity in Chat Bots

2018 ◽  
Vol 2 (3) ◽  
pp. 60 ◽  
Author(s):  
Mario Neururer ◽  
Stephan Schlögl ◽  
Luisa Brinkschulte ◽  
Aleksander Groth
Keyword(s):  
Intelligent Agents ◽  
Social Intelligence ◽  
Virtual Agents ◽  
Learning From Experience ◽  
Alan Turing ◽  
Conversational Behavior ◽  
Artificial Intelligence Technology ◽  
Intelligent Virtual Agents ◽  
Open Question ◽  
Question Survey

In 1950, Alan Turing proposed his concept of universal machines, emphasizing their abilities to learn, think, and behave in a human-like manner. Today, the existence of intelligent agents imitating human characteristics is more relevant than ever. They have expanded to numerous aspects of daily life. Yet, while they are often seen as work simplifiers, their interactions usually lack social competence. In particular, they miss what one may call authenticity. In the study presented in this paper, we explore how characteristics of social intelligence may enhance future agent implementations. Interviews and an open question survey with experts from different fields have led to a shared understanding of what it would take to make intelligent virtual agents, in particular messaging agents (i.e., chat bots), more authentic. Results suggest that showcasing a transparent purpose, learning from experience, anthropomorphizing, human-like conversational behavior, and coherence, are guiding characteristics for agent authenticity and should consequently allow for and support a better coexistence of artificial intelligence technology with its respective users.

scholarly journals Towards Learning ‘Self’ and Emotional Knowledge in Social and Cultural Human-Agent Interactions

2011 ◽  
pp. 305-324
Author(s):  
Wan Ching Ho ◽  
Kerstin Dautenhahn ◽  
Meiyii Lim ◽  
Sibylle Enz ◽  
Carsten Zoll ◽  
...  
Keyword(s):  
Autonomous Agents ◽  
Virtual Learning Environment ◽  
Virtual Agents ◽  
Cultural Backgrounds ◽  
Social Agents ◽  
Agent Interactions ◽  
Human Agent ◽  
The World ◽  
Intelligent Virtual Agents ◽  
Emotional Knowledge

This article presents research towards the development of a virtual learning environment (VLE) inhabited by intelligent virtual agents (IVAs) and modelling a scenario of inter-cultural interactions. The ultimate aim of this VLE is to allow users to reflect upon and learn about intercultural communication and collaboration. Rather than predefining the interactions among the virtual agents and scripting the possible interactions afforded by this environment, we pursue a bottom-up approach whereby inter-cultural communication emerges from interactions with and among autonomous agents and the user(s). The intelligent virtual agents that are inhabiting this environment are expected to be able to broaden their knowledge about the world and other agents, which may be of different cultural backgrounds, through interactions. This work is part of a collaborative effort within a European research project called eCIRCUS. Specifically, this article focuses on our continuing research concerned with emotional knowledge learning in autobiographic social agents.

scholarly journals Chapter 9 - Theory of Mind and Joint Attention - The Handbook on Socially Interactive Agents

2021 ◽  
Author(s):  
Jairo Pérez-Osorio ◽  
Eva Wiese ◽  
Agnieszka Wykowska
Keyword(s):  
Theory Of Mind ◽  
Joint Attention ◽  
Cognitive Neuroscience ◽  
Computational Models ◽  
Social Cognitive ◽  
Virtual Agents ◽  
Artificial Agents ◽  
Future Directions ◽  
Social Cognitive Neuroscience ◽  
Intelligent Virtual Agents

The present chapter provides an overview from the perspective of social cognitive neuroscience (SCN) regarding theory of mind (ToM) and joint attention (JA) as crucial mechanisms of social cognition and discusses how these mechanisms have been investigated in social interaction with artificial agents. In the final sections, the chapter reviews computational models of ToM and JA in social robots (SRs) and intelligent virtual agents (IVAs) and discusses the current challenges and future directions.

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