Modelling Machine Emotions for Realizing Intelligence: Foundations and Applications (Smart Innovation, Systems and Technologies, 01)

✍ Scribed by Toyoaki Nishida (editor), Colette Faucher (editor)

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 195
• Category: Library

✦ Synopsis

Emotion connects the thought to the body, which is a magnificent biological - vice for sensing and affecting the world. The thought controls the body through emotions. The body affects the thought through emotions. Through this mec- nism, the thought allows the agent to behave intelligently in the complex world filled with a huge amount of dynamic information. The emotion maps a flux of information into a space which the agent is familiar with, enabling her/him to associate ongoing events with past experiences which help to reduce complexity by providing with a nominal solution. Recent findings in brain science suggest that mirror neurons map visual signals into motor signals for the body. This mechanism might permit one to experience the emotion of the other agent just by feeling the motor signals caused by mirror neurons as a result of visual stimuli caused by the other agent’s emotional beh- iors. In particular, it might play a significant role in invoking empathy in a social situation. It may not be hard to think about what might happen to emotion-less machines. The emotion-less machines may not be able to accumulate experiences to avoid serious failures. They may not be able to communicate with the humans in an empathetic way.

✦ Table of Contents

Title
Foreword
Editors
Preface
Table of Contents
Advances in Multimedia Services in Intelligent Environments – Software Development Challenges and Solutions
Introduction
Conclusions
References and Further Reading
Evaluating the Generality of a Life-Cycle Framework for Incorporating Clustering Algorithms in Adaptive Systems
Introduction
The Software Life Cycle
Inception
Defining Requirements for the Prototype System and Analysis and Design of the Prototype Adaptive Recommender System
Building and Evaluating the Prototype Adaptive Recommender System
Elaboration
Computing the Resemblance Coefficients for the Data Set and Developing the Clustering Algorithm
Execute the Clustering Method for the Prototype and Evaluating the Results of the Clustering Algorithm used in the Prototype
Construction
The Most Efficient Algorithm and Designing Stereotypes Based on This Algorithm
Building the User Modeling Component Based on the Stereotypes and Incorporating Them into the System
Transition-Dynamically Improving System Performance while Used by Real Users
Conclusion about the Life Cycle Process
References
A Distributed Multimedia Data Management over the Grid
Introduction
Related Work
Overall Framework
Replicated Multidimensional Index Structure
Distributed Query Processing
Automatic Load Balancing
Empirical Study
Conclusion and Future Works
References
A View of Monitoring and Tracing Techniques and Their Application to Service-Based Environments
Introduction
Execution Monitoring and Tracing
Categories of Execution Monitoring
What Can Be Traced?
Monitoring Techniques
Comparison
Monitoring Challenges for Multimedia Services
Behavioral Change
Information Access
Timing
Synchronization of Event Traces
Distributed Monitoring
Trace Transmission
Conclusion
References
An Ontological SW Architecture SupportingContribution and Retrieval of Service and Process Models
Introduction
An Application in the Healthcare Domain
Ontological SW Architecture
Three Tier with Ontologies
Encoding Procedures through Ontologies
Enabling Direct Contribution through Ontologies
Enabling Semantic Queries
Enabling Adapted Presentation through Ontologies
Conclusions
References
Model-Driven Quality Engineering of Service-Based Systems
Introduction
Related Work
QoS-Enabled WSDL (Q-WSDL)
QoS Characteristics of Web Services
Q-WSDL Metamodel
Composite Web Services
QoS Prediction of Composite Web Services
Model-Driven Reliability Prediction
Example Application
Conclusions
References
Application of Logic Models for Pervasive Computing Environments and Context-Aware Services Support
Introduction
Contextual Models Requirements for Pervasive Computing
The LogiPerSe First Order Logic Model
Partial Validation and Ambiguity
Scalability
Composability
Pervasive Service Scenario
Logic-Based System Architecture
Logic Model – Object-Oriented Model - Ontology Integration
Conclusions
References
Intelligent Host Discovery from Malicious Software
Introduction
Instant Message Clients
Search Engines
Social Networks
Torrent Trackers
Conclusions
References
Swarm Intelligence: The Ant Paradigm
Introduction
Swarm Intelligence
Principles of Natural Swarms
The Biological Origins of Ant Algorithms
Ant Algorithms of the Ant Colony Optimization Framework
Ant System
MAX MIN Ant System
Ant Colony System
Conclusion
References
Formulating Discrete Geometric Random Sums for Facilitating Intelligent Behaviour of a Complex System under a Major Risk
Introduction
Formulation of a Discrete Geometric Random Sum
Stochastic Derivation of a Discrete Geometric Random Sum
Risk and Crisis Management Applications and Computational Intelligence Interpretation of a Discrete Geometric Random Sum
Conclusions
References
Incorporating a Discrete Renewal Random Sum in Computational Intelligence and Cindynics
Introduction
Formulation of a Discrete Renewal Random Sum
Stochastic Derivation of a Discrete Renewal Random Sum
Application in Cindynics and Interpretation in Computational Intelligence of a Discrete Renewal Random Sum
Conclusions
References
Author Index