3,987 research outputs found
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Modeling Factions for \u27Effects Based Operations\u27: Part I Leader and Follower Behaviors
This paper presents a synthetic approach for generating role playing simulation games intended to support analysts (and trainees) interested in testing alternative competing courses of action (operations) and discovering what effects they are likely to precipitate in potential ethno-political conflict situations. Simulated leaders and followers capable of playing these games are implemented in a cognitive modeling framework, called PMFserv, which covers value systems, personality and cultural factors, emotions, relationships, perception, stress/coping style and decision making. Of direct interest, as Sect. 1.1 explains, is mathematical representation and synthesis of best-of-breed behavioral science models within this framework to reduce dimensionality and to improve the realism and internal validity of the agent implementations. Sections 2 and 3 present this for leader profiling instruments and group membership decision-making, respectively. Section 4 serves as an existence proof that the framework has generated several training and analysis tools, and Sect. 5 concludes with lessons learned. Part II turns to the question of assessment of the synthesis and its usage in course of action studies
From Polygraphs to Truth Machines: Artificial Intelligence in Lie Detection
The proliferation of artificial intelligence (AI)-enhanced lie detection tools in business, educational, community, and governmental contexts signals a new era of deception detection. With these AI developments, collections of intimate biometric information such as facial and retinal data, keystroke patterns, brain scans, and physiological changes in the cardiovascular system are combined with personal profiles to produce analyses of a subject’s supposed veracity. This article explores some early lie detection technologies (such as the polygraph) and discusses the influences that lie detection initiatives have had in human interactions through the decades. It addresses the empirical issues of whether specific AI technologies have the capability of recognizing lying along with the related cultural concerns involving the proliferation of lie detection implementations. It analyzes the appropriateness of using invasive and often unreliable new AI methodologies for lie detection in comparison with previous methods such as the polygraph. The article also examines ethical and cultural concerns involving the obtaining and analyzing of such intimate data. It analyzes the subordinate statuses of the human subjects of lie detection as well as issues of consent for those that are faced with complex and often opaque systems. Whatever the answers to questions about reliability and mental privacy, many AI-enabled lie detection technologies are currently being used in security and police procedures, employment interviewing, and as part of anti-cheating educational initiatives
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
Autonomous Decision-Making based on Biological Adaptive Processes for Intelligent Social Robots
Mención Internacional en el título de doctorThe unceasing development of autonomous robots in many different scenarios drives a
new revolution to improve our quality of life. Recent advances in human-robot interaction
and machine learning extend robots to social scenarios, where these systems pretend
to assist humans in diverse tasks. Thus, social robots are nowadays becoming real in
many applications like education, healthcare, entertainment, or assistance. Complex
environments demand that social robots present adaptive mechanisms to overcome
different situations and successfully execute their tasks. Thus, considering the previous
ideas, making autonomous and appropriate decisions is essential to exhibit reasonable
behaviour and operate well in dynamic scenarios.
Decision-making systems provide artificial agents with the capacity of making
decisions about how to behave depending on input information from the environment.
In the last decades, human decision-making has served researchers as an inspiration to
endow robots with similar deliberation. Especially in social robotics, where people expect
to interact with machines with human-like capabilities, biologically inspired decisionmaking
systems have demonstrated great potential and interest. Thereby, it is expected
that these systems will continue providing a solid biological background and improve the
naturalness of the human-robot interaction, usability, and the acceptance of social robots
in the following years.
This thesis presents a decision-making system for social robots acting in healthcare,
entertainment, and assistance with autonomous behaviour. The system’s goal is to
provide robots with natural and fluid human-robot interaction during the realisation of
their tasks. The decision-making system integrates into an already existing software
architecture with different modules that manage human-robot interaction, perception,
or expressiveness. Inside this architecture, the decision-making system decides which
behaviour the robot has to execute after evaluating information received from different
modules in the architecture. These modules provide structured data about planned
activities, perceptions, and artificial biological processes that evolve with time that are the
basis for natural behaviour. The natural behaviour of the robot comes from the evolution
of biological variables that emulate biological processes occurring in humans. We also
propose a Motivational model, a module that emulates biological processes in humans for
generating an artificial physiological and psychological state that influences the robot’s
decision-making. These processes emulate the natural biological rhythms of the human organism to produce biologically inspired decisions that improve the naturalness exhibited
by the robot during human-robot interactions. The robot’s decisions also depend on what
the robot perceives from the environment, planned events listed in the robot’s agenda, and
the unique features of the user interacting with the robot.
The robot’s decisions depend on many internal and external factors that influence how
the robot behaves. Users are the most critical stimuli the robot perceives since they are
the cornerstone of interaction. Social robots have to focus on assisting people in their
daily tasks, considering that each person has different features and preferences. Thus,
a robot devised for social interaction has to adapt its decisions to people that aim at
interacting with it. The first step towards adapting to different users is identifying the user
it interacts with. Then, it has to gather as much information as possible and personalise
the interaction. The information about each user has to be actively updated if necessary
since outdated information may lead the user to refuse the robot. Considering these facts,
this work tackles the user adaptation in three different ways.
• The robot incorporates user profiling methods to continuously gather information
from the user using direct and indirect feedback methods.
• The robot has a Preference Learning System that predicts and adjusts the user’s
preferences to the robot’s activities during the interaction.
• An Action-based Learning System grounded on Reinforcement Learning is
introduced as the origin of motivated behaviour.
The functionalities mentioned above define the inputs received by the decisionmaking
system for adapting its behaviour. Our decision-making system has been designed
for being integrated into different robotic platforms due to its flexibility and modularity.
Finally, we carried out several experiments to evaluate the architecture’s functionalities
during real human-robot interaction scenarios. In these experiments, we assessed:
• How to endow social robots with adaptive affective mechanisms to overcome
interaction limitations.
• Active user profiling using face recognition and human-robot interaction.
• A Preference Learning System we designed to predict and adapt the user
preferences towards the robot’s entertainment activities for adapting the interaction.
• A Behaviour-based Reinforcement Learning System that allows the robot to learn
the effects of its actions to behave appropriately in each situation.
• The biologically inspired robot behaviour using emulated biological processes and
how the robot creates social bonds with each user.
• The robot’s expressiveness in affect (emotion and mood) and autonomic functions
such as heart rate or blinking frequency.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Richard J. Duro Fernández.- Secretaria: Concepción Alicia Monje Micharet.- Vocal: Silvia Ross
EVALUACIÓN COMPUTARIZADA DE PRUEBAS PSICOLÓGICAS MEDIANTE EL PROCESAMIENTO DIGITAL DE IMÁGENES
Este trabajo presenta un algoritmo desarrollado en MATLAB® para la evaluación computarizadade pruebas psicológicas realizadas con la técnica de lápiz-papel de opción múltiple. Con la utilización adecuada de las herramientas actuales para el procesamiento digital de imágenes y reconocimiento de patrones, es posible la evaluación automática de cualquier prueba psicológica realizada con la técnica de lápiz-papel utilizando una computadora personal convencional. En este caso, para demostrar el potencial del algoritmo, fue adaptado para la escala básica del Inventario Multifásico de la Personalidad de Minnesota para individuos masculinos. Los resultados obtenidos demuestran 99.5% de exactitud y la obtención de la gráfica del perfil en 40 segundos. Con el formato digitalizado de la hoja de respuestas, el algoritmo realiza el acondicionamiento de la imagen, la detección y clasificación de los ítems y la contabilidad de las respuestas; finalmente, brinda la gráfica del perfil del individuo
ENHANCING PRIVACY IN MULTI-AGENT SYSTEMS
La pérdida de privacidad se está convirtiendo en uno de los mayores problemas
en el mundo de la informática. De hecho, la mayoría de los usuarios
de Internet (que hoy en día alcanzan la cantidad de 2 billones de usuarios
en todo el mundo) están preocupados por su privacidad. Estas preocupaciones
también se trasladan a las nuevas ramas de la informática que están
emergiendo en los ultimos años. En concreto, en esta tesis nos centramos en
la privacidad en los Sistemas Multiagente. En estos sistemas, varios agentes
(que pueden ser inteligentes y/o autónomos) interactúan para resolver problemas.
Estos agentes suelen encapsular información personal de los usuarios
a los que representan (nombres, preferencias, tarjetas de crédito, roles, etc.).
Además, estos agentes suelen intercambiar dicha información cuando interactúan entre ellos. Todo esto puede resultar en pérdida de privacidad para
los usuarios, y por tanto, provocar que los usuarios se muestren adversos a
utilizar estas tecnologías.
En esta tesis nos centramos en evitar la colección y el procesado de información personal en Sistemas Multiagente. Para evitar la colección de información, proponemos un modelo para que un agente sea capaz de decidir
qué atributos (de la información personal que tiene sobre el usuario al que
representa) revelar a otros agentes. Además, proporcionamos una infraestructura
de agentes segura, para que una vez que un agente decide revelar
un atributo a otro, sólo este último sea capaz de tener acceso a ese atributo,
evitando que terceras partes puedan acceder a dicho atributo. Para evitar el
procesado de información personal proponemos un modelo de gestión de las
identidades de los agentes. Este modelo permite a los agentes la utilización
de diferentes identidades para reducir el riesgo del procesado de información. Además, también describimos en esta tesis la implementación de dicho
modelo en una plataforma de agentes.Such Aparicio, JM. (2011). ENHANCING PRIVACY IN MULTI-AGENT SYSTEMS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/13023Palanci
Operator-based approaches to harm minimisation in gambling: summary, review and future directions
In this report we give critical consideration to the nature and effectiveness of harm
minimisation in gambling. We identify gambling-related harm as both personal (e.g.,
health, wellbeing, relationships) and economic (e.g., financial) harm that occurs from
exceeding one’s disposable income or disposable leisure time. We have elected to use the
term ‘harm minimisation’ as the most appropriate term for reducing the impact of
problem gambling, given its breadth in regard to the range of goals it seeks to achieve,
and the range of means by which they may be achieved.
The extent to which an employee can proactively identify a problem gambler in a
gambling venue is uncertain. Research suggests that indicators do exist, such as sessional
information (e.g., duration or frequency of play) and negative emotional responses to
gambling losses. However, the practical implications of requiring employees to identify
and interact with customers suspected of experiencing harm are questionable,
particularly as the employees may not possess the clinical intervention skills which may
be necessary. Based on emerging evidence, behavioural indicators identifiable in industryheld
data, could be used to identify customers experiencing harm. A programme of
research is underway in Great Britain and in other jurisdiction
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