TY  - BOOK
AU  - Nassiri-Mofakham,Faria
TI  - Intelligent Computational Systems: A Multi-Disciplinary Perspective
T2  - Current and Future Developments in Artificial Intelligence
SN  - 9781681085029
AV  - Q335.I584 2017 
U1  - 6.3 
PY  - 2017///
CY  - Sharjah
PB  - Bentham Science Publishers
KW  - Artificial intelligence--Congresses
KW  - Electronic books
N1  - Intro -- CONTENTS -- FOREWORD -- PREFACE -- List of Contributors -- PART I: SIMULATION -- Simulation, Intelligence and Agents: Exploring the Synergy -- Nasser Ghasem-Aghaee1,2,*, Tuncer Ören3 and Levent Yilmaz4 -- 1. INTRODUCTION -- 2. SIMULATION: HIGHLIGHTS -- 2.1. Stand-alone Simulation -- 2.2. Embedded Simulation -- 2.3. Other Perspectives -- 3. INTELLIGENCE, INTELLIGENT ENTITIES, AND AGENTS -- 3.1. Types of Intelligence -- 3.1.1. Entities -- 3.1.2. Context -- 3.3. Components -- 3.4. Agents -- 3.5. Software for Agents -- 4. SYNERGIES OF SIMULATION AND AGENTS -- 5. AGENT SIMULATION -- 5.1. Applications -- 5.2. Methodology -- 5.3. Software for Agent Simulation -- 6. AGENT-SUPPORTED SIMULATION -- 7. AGENT-MONITORED SIMULATION -- 8. SOME PROMISING RESEARCH AND DEVELOPMENT AREAS -- CONCLUSION -- NOTES -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Living with Digital Worlds: A Personal View of Artificial Intelligence -- Helder Coelho* -- 1. INTRODUCTION -- 2. ROAD MAP: TERRITORIES -- 3. MODELS -- 4. HUMAN INGENUITY -- 5. MECHANISMS -- 6. MACHINE LEARNING VARIETY -- 7. AGENT SHAPES -- 8. PREDICTING THE FUTURE -- 9. CHALLENGES -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Baseline for Nonlinear Bilateral Negotiations: The full results of the agents competing in ANAC 2014 -- Reyhan Aydoğan1,2,*, Catholijn M. Jonker2, Katsuhide Fujita3, Tim Baarslag4, Takayuki Ito5, Rafik Hadfi5 and Kohei Hayakawa5 -- 1. INTRODUCTION -- 2. ANAC 2014 -- 2.1. ANAC 2014 Rules -- 2.2. Negotiation Scenarios -- 2.3. Competition Setup -- 3. ANAC 2014 AGENTS -- 3.1. AgentM [41] -- 3.2. AgentYK [42] -- 3.3. BraveCat [43] -- 3.4. DoNA [44] -- 3.5. E2Agent [45] -- 3.6. Gangster [46] -- 3.7. Group2Agent [47] -- 3.8. k-GAgent [49] -- 3.9. Sobut -- 3.10. WhaleAgent [51] -- 4. RESULTS OF ANAC 2014 COMPETITION -- 4.1. Qualifying Round; 4.2. Final Round -- 5. IN DEPTH EVALUATION OF ANAC 2014 AGENTS -- 5.1. Experimental Setup -- 5.2. Experiment Results -- 5.3. Effect of Domain Size -- 5.4. Effect of Constraint Size -- 5.5. Effect of Constraint-Issue Distribution -- CONCLUSION -- NOTES -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Multi Agent Model for Reverse Perception Effect -- Nuno Trindade Magessi* and Luis Antunes -- 1. INTRODUCTION -- 2. EXPLAINING PERCEPTION -- 3. GOING AROUND THEORIES -- 3.1. Direct Perception -- 3.2. Perception in Action -- 3.3. Evolutionary Psychological And Perception -- 3.4. Structural Information Theory -- 3.5. Interface Theory -- 3.6. Empirical Perception Theory -- 4. THE GAP BETWEEN PERCEPTION AND REALITY -- 5. STIMULI AND PERCEPTIBLES -- 6. THE FILTER OF CULTURE IN PERCEIVING REALITY -- 7. INSIDE THE PERCEPTION PROCESS OF REALITY -- 8. AIDS AS A CASE STUDY -- 9. AIDS PERCEPTION SIMULATOR MODEL -- 10. EXPLORING OUTPUT RESULTS -- 11. DISCUSSING RESULTS -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- PART II: INTERACTION WITH HUMANS -- Lexicon-based Sentiment Analysis in Persian -- Mohammad Ehsan Basiri1,*, Nasser Ghasem-Aghaee2,3 and Ahmad Reza Naghsh-Nilchi2 -- 1. INTRODUCTION -- 2. RELATED WORK -- 2.1. Sentiment Analysis -- 2.2. Sentiment Analysis in Persian -- 2.3. Sentiment Strength Detection -- 3. PROPOSED SYSTEM -- 3.1. Normalization -- 3.1. Example 1: -- 3.2. Spelling Correction -- 3.2. Example 2: -- 3.3. Stemming -- 3.4. Sentence Splitting -- 3.5. Strength Detection -- 3.6. Score Aggregation -- 3.7. Research Questions -- 4. EXPERIMENTS -- 4.1. Datasets and Evaluation Metrics -- 4.2. Results and Discussions -- 4.2. Example 3: -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES; The Age of the Connected World of Intelligent Computational Entities: Reliability Issues including Ethics, Autonomy and Cooperation of Agents -- Tuncer Ören1,* and Levent Yilmaz2 -- 1. INTRODUCTION -- 1.1. Significance of the Problem -- 1.2. Motivating Scenarios -- 1.3. Organization of the Chapter -- 2. CONNECTED WORLD -- 2.1. Characteristics of the Connected World -- 2.2. Some Examples for Connected Entities -- 3. THE EVOLUTION OF THE CONNECTED WORLD -- 3.1. Hand Tools -- 3.2. Power Tools (Industrial Age) -- 3.3. Knowledge Processing Tools (Information Age/Informatics age) -- 3.3.1. Advancements in Knowledge Processing Tools -- 3.3.2. Advancements in Entities with Additional Knowledge Processing Abilities -- 3.4. Smart Tools and Intelligent Tools (Cybernetic Age) -- 3.5. Connected Tools (Connected World of Intelligent Computational Entities) -- 3.6. Superintelligence (Post-human Era?) -- 4. WHAT MIGHT GO WRONG IN THE AGE OF THE CONNECTED WORLD -- 4.1. Approaches for Basic Sources of Failures -- 4.2. Some Counterintuitive Views of Autonomy and Cooperation -- 4.2.1. Autonomy -- 4.2.2. Cooperation -- 4.3. Ethics and its Limitations (in Uncivilized Environments) -- 4.3.1. Design Strategies for Ethical Agents -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- P-UTADIS: A Multi Criteria Classification Method -- Majid Esmaelian1,*, Hadi Shahmoradi1 and Fateme Nemati2 -- 1. INTRODUCTION -- 2. CLASSIFICATION -- 2.1. Review of Classification Techniques -- 2.1.1. Common Techniques in Data Classification Problems -- 2.1.2. Common Techniques in Data Classification with Ordinal Class -- 2.2. Multi Criteria Decision Aid Classification Technique -- 2.2.1. UTilities Additives DIScriminantes (UTADIS) -- 3. EXTENSION OF THE UTADIS WITH POLYNOMIAL AND GA-PSO ALGORITHM IN CLASSIFICATION -- 3.1. P-UTADIS vs. UTADIS -- 3.2. Preliminaries; 3.2.1. Genetic Algorithm (GA) -- 3.2.2. Particle Swarm Optimization Algorithm (PSO) -- 3.3. P-UTADIS Method -- 3.3.1. Methodology -- 3.3.2. Algorithm Steps -- 3.3.3. P-UTADIS performance on IRIS Data Set -- 3.3.4. Comparison of P-UTADIS Performance versus UTADIS -- 3.4. Experimental Study -- 3.4.1. Test Problems -- 3.4.2. Algorithms for Comparison -- 3.4.3. Results and Discussion -- 3.5. P-UTADIS Time Complexity -- CONCLUDING REMARKS -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- PART III: APPLICATIONS -- Artificial Intelligence Techniques for Credit Risk Management -- Abdolreza Nazemi* and Konstantin Heidenreich -- 1. INTRODUCTION -- 2. SUPPORT VECTOR REGRESSION MODELING FOR RECOVERY RATES -- 3. EMPIRICAL ANALYSIS -- 3.1. Selection of factors for modeling -- 3.2. Exploratory data analysis -- 4. EMPIRICAL MODELLING RESULTS -- CONCLUSION -- NOTES -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Novel Task-Driven Sensor-Management Method in Multi-Object Filters Using Stochastic Geometry -- Amirali K. Gostar*, Reza Hoseinnezhad and Alireza Bab-Hadiashar -- 1. INTRODUCTION -- 1.1. Multi-Sensor Management -- 1.2. Sensor-Selection and Sensor-Control in Target Tracking Scenarios -- 2. BACKGROUND -- 2.1. Sensor Management Solution Framework --  Prediction --  Pre-Estimation --  Pseudo-Measurements --  Pseudo-Update --  Objective Function --  Decision Making --  Update -- 3. ASSUMPTIONS -- 3.1. Single-Step Look-Ahead -- 3.2. Pseudo-Measurement Approximation -- 4. OBJECTIVE FUNCTION -- 4.1. Task-driven Approach -- 4.2. Information-driven Approach -- 5. COMMON OBJECTIVE FUNCTIONS IN SENSOR MANAGEMENT STUDIES -- 5.1. Rényi Divergence -- 5.2. The Posterior Expected Number of Targets -- 5.3. The Cardinality-Variance Based Objective Function -- 6. RANDOM FINITE SET BASED MULTI-TARGET FILTER -- 6.1. Multi-Target System Model; 6.2. Stochastic Model for Multi-Target State Evolution -- 6.3. Stochastic Model for Multi-Target State Measurement -- 6.4. Multi-Object Bayes Recursion -- 6.5. Poisson RFS -- 6.6. IID Cluster RFS -- 6.7. Bernoulli RFS -- 6.8. Multi-Bernoulli RFS -- 7. LABELED MULTI-BERNOULLI FILTER -- 7.1. Prediction -- 7.2. Update -- 7.3. Implementation -- 8. LABELED MULTI-BERNOULLI -- 8.1. Sensor-Control -- 8.2. Cost Function -- 8.3. Implementation -- 8.4. Computing the Cost -- 9. OSPA METRIC -- 10. NUMERICAL STUDIES -- CONCLUSIONS AND FUTURE STUDIES -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Parallel Processing in Holonic Systems -- Imane Basiry1,* and Nasser Ghasem-Aghaee2 -- 1. INTRODUCTION -- 2. LITERATURE REVIEW -- 3. FIPA STANDARD AND AGENTS COMMUNICATION LANGUAGE -- 4. DESIGNING A HOLONIC MODEL -- 5. MODEL DESIGN AND ANALYSIS -- 5.1. First Level of the Model -- I. First level: Structural Analysis -- II. First Level: Behavioral Analysis -- III. First Level: Matching the Model to an Airport Control Systems -- IV. First Level: Matching the Model to Factory Control Systems -- 5.2. Second Level of the Model -- I. Second Level: Structural Analysis -- II. Second Level: Behavioral Analysis -- III. Second Level: Matching the Model to an Airport Control System -- IV. Second Level: Matching the Model to a Factory Control System -- 5.3. Third Level of the Model -- I. Third Level: Structural Analysis -- II. Third Level: Behavioral Analysis -- III. Third Level: Matching the Model to Airport Control Systems -- IV. Third Level: Matching the Model to Factory Control Systems -- 6. PREPARING THE MODEL FOR CRITICAL CONDITIONS -- 7. IMPLEMENTATION AND NUMERIC EVALUATION IN THE FACTORY TEST CASE -- 8. REVIEW OF PROPOSED MODEL FEATURES -- CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES; Robot-Assisted Language Learning: Artificial Intelligence in Second Language Acquisition
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