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Advances in Agent-Based Complex Automated Negotiations

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Complex Automated Negotiations have been widely studied and are becoming an important, emerging area in the field of Autonomous Agents and Multi-Agent Systems. In general, automated negotiations can be complex, since there are a lot of factors that characterize such negotiations. These factors include the number of issues, dependency between issues, representation of utility, negotiation protocol, negotiation form (bilateral or multi-party), time constraints, etc. Software agents can support automation or simulation of such complex negotiations on the behalf of their owners, and can provide them with adequate bargaining strategies. In many multi-issue bargaining settings, negotiation becomes more than a zero-sum game, so bargaining agents have an incentive to cooperate in order to achieve efficient win-win agreements. Also, in a complex negotiation, there could be multiple issues that are interdependent. Thus, agent’s utility will become more complex than simple utility functions. Further, negotiation forms and protocols could be different between bilateral situations and multi-party situations. To realize such a complex automated negotiati on, we have to incorporate advanced Artificial Intelligence technologies includes search, CSP, graphical utility models, Bays nets, auctions, utility graphs, predicting and learning methods. Applications could include e-commerce tools, decisionmaking support tools, negotiation support tools, collaboration tools, etc.

These issues are explored by researchers from different communities in Autonomous Agents and Multi-Agent systems. They are, for instance, being studied in agent negotiation, multi-issue negotiations, auctions, mechanism design, electronic commerce, voting, secure protocols, matchmaking & brokering, argumentation, and co-operation mechanisms. This book is also edited from some aspects of negotiation researches including theoretical mechanism design of trading based on auctions, allocation mechanism based on negotiation among multi-agent, case-study and analysis of automated negotiations, data engineering issues in negotiations, and so on.

Author(s): Takayuki Ito, Minjie Zhang, Valentin Robu, Shaheen Fatima, Tokuro Matsuo
Series: Studies in Computational Intelligence 233
Edition: 2009
Publisher: Springer
Year: 2009

Language: English
Pages: 216

front-matter.pdf......Page 1
What Is Emotional Intelligence?......Page 15
Typical Characteristics of Emotion......Page 18
Basic Components of Emotion......Page 19
The Feeling Component......Page 20
Rationality of Emotion......Page 21
Regulation and Control of Emotion......Page 22
The Biological Basis of Emotion......Page 24
An Affective Neuro Scientific Model......Page 25
Self Regulation Models of Emotion......Page 27
Emotional Learning......Page 31
Mathematical Modeling of Emotional Dynamics......Page 32
Controlling Emotion by Artificial Means......Page 35
Effect of Emotion Modeling on Human Machine Interactions......Page 36
Scope of the Book......Page 37
Exercises......Page 39
References......Page 45
Introduction......Page 48
System Modeling and Stability......Page 49
Stability Analysis of Dynamics by Lyapunov Energy Functions......Page 53
Stability Analysis for Continuous Dynamics......Page 55
Mamdani Type Fuzzy Systems......Page 58
Takagi-Sugeno Type Fuzzy Systems......Page 59
Stability Analysis of T-S Fuzzy Systems......Page 61
Chaotic Neuro Dynamics and Lyapunov Exponents......Page 65
Emotional Dynamics and Stability Analysis......Page 67
The Lyapunov Exponents and the Chaotic Emotional Dynamics......Page 68
Exercises......Page 71
References......Page 73
Introduction......Page 75
Discrete Fourier and Cosine Transforms......Page 76
Median Filtering......Page 78
Thresholding......Page 79
Boundary Detection Algorithms......Page 81
Region Oriented Segmentation Algorithm......Page 87
Chain Codes......Page 96
Regional Descriptors......Page 98
Unsupervised Clustering......Page 99
Image Matching......Page 100
Template Matching......Page 101
Scene Interpretation......Page 102
References......Page 103
Introduction......Page 105
Filtering, Segmentation and Localization of Facial Components......Page 107
Segmentation of the Mouth Region......Page 108
Segmentation of the Eye-Region......Page 109
Determination of the Length of Eyebrow-Constriction......Page 111
The Fuzzy Relational Model for Emotion Detection......Page 113
Experiments and Results......Page 116
Validation of the System Performance......Page 124
The Model......Page 125
Properties of the Model......Page 127
Emotion Control by Mamdani’s Model......Page 129
Experiments and Results......Page 135
Exercises......Page 138
References......Page 143
Introduction......Page 146
Animal Studies on Amygdale......Page 147
Fear and Threat Perception of the Amygdale......Page 148
Neuro-psychology and Functional Neuro-imaging Studies on OFC Behavior......Page 149
The Anterior Cingulated......Page 150
Emotion Monitoring by the Cingulated Cortex......Page 151
The Anterior Cingulate Cortex as a Self-regulatory Agent......Page 152
Voluntary Self-regulation of Emotion......Page 154
fMRI Studies on Voluntary Regulation of Sexual Arousals......Page 155
Neural Circuitry Underlying Emotional Self-regulation......Page 156
EEG Conditioning and Affective Disorders......Page 157
Pain Conditioning in Rats......Page 158
EEG Analysis for Premenstrual Dysphoric Disorder......Page 159
Emotional Dysregulation in Childhood from Non-clinical Samples......Page 160
Clinical Samples for Emotional Dysregulation for Children......Page 161
Emotion Regulation in Adulthood......Page 162
Conclusions......Page 163
Exercises......Page 164
References......Page 177
Introduction......Page 186
Stable Points of Non-temporal Logic......Page 187
Finding Common Interpretations of Propositional Statements......Page 188
Determining Stable Points of Logical Statements......Page 190
Stable Points in Propositional Temporal Logic......Page 191
Stability Analysis of Propositional Temporal System......Page 194
Human Emotion Modeling and Stability Analysis......Page 196
Stability Analysis of the Emotional Dynamics......Page 197
Weight Adaptation in Emotion Dynamics by Hebbian Learning......Page 200
The Fuzzy Temporal Representation of Phenomena Involving Emotional States......Page 201
Stabilization of Emotional Dynamics......Page 205
Psychological Stability in Emotion-Logic Counter-Actions......Page 206
Conclusions......Page 208
Exercises......Page 209
References......Page 218
Introduction......Page 219
Proposed Model for Chaotic Emotional Dynamics......Page 220
Variation in a$_ii$......Page 222
Variation in c$_ij$......Page 224
Variation of $b_ij$......Page 225
Chaotic Fluctuation in Emotional State......Page 227
Stability Analysis of the Proposed Emotional Dynamics by Lyapunov Energy Function......Page 229
Parameter Selection of the Emotional Dynamics by Experiments with Audio-Visual Stimulus......Page 230
A Stabilization Scheme for the Mixed Emotional Dynamics......Page 236
Conclusions......Page 238
Exercises......Page 239
References......Page 242
Introductions......Page 244
Output Interfaces......Page 246
Embodiment of Artificial Characters......Page 247
Application in Multi-agent Co-operation of Mobile Robotics......Page 248
Emotional Intelligence in Psycho-therapy......Page 249
Detection of Anti-social Motives from Emotional Expressions......Page 250
Applications in Video Photography/Movie Making......Page 252
Applications in Personality Matching of People for Matrimonial Counseling......Page 253
Synthesizing Emotions in Voice......Page 254
Application in User Assistance Systems......Page 255
Speech Articulatory Features......Page 256
Personality Building of Artificial Creatures......Page 258
Current Status......Page 260
Research Initiative at Jadavpur University......Page 261
System Identification Approach to EEG Dynamics Modeling by Evolutionary Algorithms......Page 266
Genetic Algorithm in Emotional System Identification......Page 268
Particle Swarm Optimization in Emotional System Identification......Page 269
Differential Evolution Algorithm in Emotional System Identification......Page 272
Conclusions......Page 273
References......Page 274
Introduction......Page 277
EEG Prediction by Adaptive Filtering......Page 278
LMS Filter......Page 279
EEG Prediction by NLMS Algorithm......Page 280
The RLS Filter for EEG Prediction......Page 282
The Kalman Filter for EEG Prediction......Page 284
Implication of the Results......Page 287
EEG Signal Prediction by Wavelet Coefficients......Page 289
Bio-potential Signals in Emotion Prediction......Page 294
Principles in SVM......Page 295
Emotion Clustering by Neural Networks......Page 298
References......Page 301
Introduction......Page 303
Reasoning with Emotions......Page 305
Uncertainty Management in Emotion-Based Reasoning......Page 306
Determining Manifestation of Emotion on Facial Expression from EEG Signals......Page 307
Further Readings for Researchers......Page 308
References......Page 309
back-matter.pdf......Page 313