Artificial General Intelligence
Editat de T. Saravanan, P. Preethi, Sumaya Sanober, N. Thillaiarasu, S. Balamuruganen Limba Engleză Hardback – 10 aug 2026
This comprehensive guide provides an extensive overview of the key theories, methodologies, and applied frameworks that enable AGI systems to exhibit aspects of human intelligence.
As the role of artificial intelligence grows in our everyday lives, so does the need for AI with greater capabilities. Unlike narrow AI, confined to specific tasks, artificial general intelligence seeks human-like adaptability, reasoning, and learning across domains. Integrating cognitive, mathematical, and computational concepts, it presents multidimensional solutions to create more natural human-AI interactions. This book examines the theoretical foundations, cognitive architectures, and practical methodologies shaping artificial general intelligence. It highlights the significance of human-like emotional intelligence in AI and its potential to create more natural, empathetic, and intuitive human-AI interactions, using techniques such as facial expression analysis, speech emotion recognition, and physiological signal processing. From healthcare to customer service, affective AI is being used to enhance user experiences by tailoring interactions to the emotional states of individuals. The book also discusses the ethical dilemmas posed by affective AI, such as emotional manipulation, bias in emotion detection, and the impact of AI-driven emotional decisions on human behavior. Balancing rigor with practical insight, the volume provides a roadmap for researchers, practitioners, and policymakers to study artificial general intelligence's evolution and transformative potential.
Readers will find the volume:
- Discusses different applications of affective artificial intelligence across various industries;
- Introduces the fundamental concepts of reinforcement learning for different applications;
- Presents the state-of-the-art of transfer learning analysis through contributions from industry and academia.
Audience
Engineering research scholars, students, IT professionals, network administrators, artificial intelligence and deep learning experts, and government research agencies.
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Specificații
Notă biografică
T. Saravanan, PhD is an Assistant Professor at the Gandhi Institute of Technology and Management, Bengaluru, India with more than ten years of teaching experience. He has published many research papers, book chapters, and Indian patents. His research interests include computer networks, fuzzy logic, and wireless sensor networks.
P. Preethi, PhD is an Associate Professor in the Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India. She has six books and has published 28 articles in international journals and conferences. Her areas of interest include cloud computing, network security, and machine learning.
Sumaya Sanober, PhD works in the Computer Science Department at Old Dominion University, Virginia, United States. She has published many articles in national and international journals and conferences, and serves as a reviewer on multiple boards. Her research interests include machine learning, artificial neural networks, pattern recognition, web services, cloud computing, and testing tools.
N. Thillaiarasu, PhD is an Associate Professor in the School of Computing and Information Technology, REVA University, Bangalore, India, with more than 12 years of teaching experience. He has more than 75 publications to his credit, including articles, books, and book chapters. His areas of interest include cloud computing, security, IoT, and machine learning.
S. Balamurugan, PhD is the Director, Intelligent Research Consultancy Services, Coimbatore, Tamil Nadu, India. He has published 75 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.
Cuprins
Series Preface xxi
Preface xxiii
Part I: Theoretical and Cognitive Foundations of AGI 1
1 A Unified Framework for Defining Artificial General Intelligence through Cognitive and Theoretical Perspectives 3
Latha. P., K. Sivakami, K. Selvavinayaki, V. Devi and Karthick R.
1.1 Introduction 4
1.2 Related Work 5
1.3 Proposed Methodology 9
1.4 Evaluation of the Unified AGI Framework 15
1.5 Conclusion 19
2 The Role of Theoretical Intelligence in Guiding the Development of Robust Artificial General Intelligence Systems 23
R. Mekala, Abiramasundari S., A.S. Narmadha, K. N. Jayapriya, M. Maheswaran and Karthikha Sree J.B.
2.1 Introduction 24
2.2 Related Work 28
2.3 Proposed Methodology for Developing Theoretically Grounded AGI Systems 33
2.4 Results 36
2.5 Conclusion 40
3 A Comparative Analysis of Cognitive Models Used in Symbolic and Neural Architectures for AGI 43
Beaulah David, Vanitha. G., K. Kalpana, P. Gokila, N. Logeshwari and Varnikha Sree J.B.
3.1 Introduction 44
3.2 Related Work 48
3.3 Proposed Methodology for Developing Explainable AGI through Hybrid Cognitive Architectures 52
3.4 Results 56
3.5 Conclusion 59
Part II: Cognitive Architectures and Reasoning Mechanisms 63
4 Exploring the Role of Cognitive Architectures in Building Human-Like Artificial General Intelligence Systems 65
Gokilavani A., Abhirami J. S., V. Devi, Gokila Deepa G. and Janani S.
4.1 Introduction 66
4.2 Related Work: Evolution and Divergence in CAs for AGI 69
4.3 Proposed Methodology 74
4.4 Results 79
4.5 Conclusion 83
5 Symbolic and Subsymbolic Reasoning Integration for Flexible and Context-Aware AGI Cognitive Processes 87
Saisuman Singamsetty
5.1 Introduction 88
5.2 Related Work 92
5.3 Proposed Methodology 96
5.4 Results 100Contents xi
5.5 Conclusion 103
6 Developing Common-Sense Reasoning Capabilities in AGI Using Hybrid Neural-Symbolic Learning Approaches 107
Arunkumar Medisetty and R. Asokan
6.1 Introduction 108
6.2 Related Work 113
6.3 Proposed Methodology 117
6.4 Results 120
6.5 Conclusion 123
7 Designing Explainable Artificial General Intelligence through Transparent and Interpretable Reasoning Mechanisms 127
Sudheer Singamsetty
7.1 Introduction 128
7.2 Related Work 131
7.3 Proposed Methodology 137
7.4 Results 140
7.5 Conclusion 144
Part III: Learning Paradigms for Generalization and Adaptability 147
8 Self-Supervised Learning Approaches for Enhancing the Adaptability of Artificial General Intelligence Models 149
R. Pushpalakshmi, G. Kalaiarasi, C. P. Thamil Selvi, Nithya C. and D. Satheesh Kumar
8.1 Introduction 150
8.2 Related Work 153
8.3 Proposed Methodology: SSL-Based Framework for AGI Adaptability 158
8.4 Results 160
8.5 Conclusion 164
9 Meta-Learning and Transfer Learning Techniques for Enabling Generalization in AGI Across Multiple Domains 167
B. Nagarajan, A. Jayanthi, Wasim Raja A., E. Angel Anna Prathiba, Sika K. and Kiran Kumar Thoti
9.1 Introduction 168
9.2 Related Work 172
9.3 Proposed Methodology 178
9.4 Results 182
9.5 Conclusion 186
10 Human-Level Generalization in AGI through Interactive Meta-Learning and Environmental Adaptation 189
Shylaja Chityala
10.1 Introduction 190
10.2 Related Work 194
10.3 Proposed Methodology 198
10.4 Results 203
10.5 Contextual Adaptation and Robustness under Distributional Shifts 205
10.6 Explanation Utility and User Satisfaction in Learning Tasks 206
10.7 Conclusion 206
Part IV: Decision-Making, Uncertainty, and Reinforcement Learning 209
11 Reinforcement Learning for AGI Model-Based versus Model-Free Approaches 211
Shivamma D., Shaila S.G., Ramesh Chundi and Monish L.
11.1 Introduction 212
11.2 Fundamentals of AGI 214
11.3 Model-Free Approaches in AGI 214
11.4 Model-Based Approaches in AGI 217
11.5 Model-Free versus Model-Based Approaches in AGI 222
11.6 Conclusion 223
12 InvisiDroid: Practical Evasion of ML-Based Black-Box Web Spyware Classifiers 229
M. Martinaa, S. Aravindh, S. Gokulraj and M. Tamil Thendral
12.1 Introduction 230
12.2 Related Work 232
12.3 Background 235
12.4 Proposed Methodology 236
12.5 Simulation Results 242
12.6 Conclusions 246
Part V: Multimodal Perception and Future Directions 251
13 Multimodal Perception Systems for AGI: Integrating Visual, Auditory, and Linguistic Information Sources 253
Tayar Yerramsetty
13.1 Introduction 254
13.2 Related Work 258
13.3 Proposed Methodology 262
13.4 Summary of Results in Multimodal Perception for AGI 266
13.5 Conclusion 269
14 Multimodal Learning and Perceptron-Integrating Vision, Language, and Auditory Data 273
Sindhu A., Suresh Arumugam, Shaila S.G., Monish L. and Ramesh Chundi
14.1 Introduction 275
14.2 Fundamentals of Perceptron and Neural Networks 276
14.3 Representing Different Modalities 278
14.4 Building Intelligent Systems through Multimodal Representation 279
14.5 Multimodal Fusion Techniques 282
14.6 Architectures and Models for Multimodal Learning 284
14.7 Applications of Multimodal AI 286
14.8 Challenges in Multimodal Integration 287
14.9 Future Directions in Multimodal Learning 289
14.10 Conclusion 291
15 TransGAN for Visual Anomaly Detection on Imbalanced Industrial Datasets 295
Srinivasa Perumal R., Venkatasubramanian A., Premalatha M. and Braveen M.
15.1 Introduction 296
15.2 Related Works 297
15.3 Methodology 310
15.4 Experimentation and Results 315
15.5 Conclusion and Future Work 323
16 Recent Development of Machine Learning Models for Grape Plant Disease Detection: A Review 327
M. Anuradha, G. Revathy and M. A. Mohamed Aslam
16.1 Introduction 328
16.2 Role of AI in Modern Farming 330
16.3 An Overview of ML and DL Industry in Agricultural Sector 332
16.4 Detection of Grape Leaf Disease with Transfer Learning-Based Technologies 344
16.5 Results and Discussion 348
16.6 Conclusion 351
17 Precision Plant Pathology: Real-Time Disease Detection Using Deep Learning 353
M. Martinaa, Pokkuluri Kiran Sree, S. Senthilvadivu and M. Shyamalagowri
17.1 Introduction 354
17.2 Related Works 356
17.3 Proposed Methodology 359
17.4 Experimental Results 368
17.5 Conclusion 374
18 Combining AI and Sensor Fusion Technology for Improving Mobility Solutions Designed for Visually Impaired Users 377
M.A. Mohamed Aslam, G. Revathy, A. Gayathri, M. Krithika and B. M. Shruthi
18.1 Introduction 378
18.2 Various Datasets for Training DL Models 380
18.3 Approaches in the Area of Item Detection and Distance Estimation 383
18.4 Aids for Visual Object Recognition and Distance Measuring 395
18.5 Evaluation Criteria and Comparative Study 397
18.6 Object Identification at Varying Distances 399
18.7 Conclusion 400
19 Future of Artificial General Intelligence: Quantum Computing, Brain-Computer Interfaces, and AGI Evolution 405
Youddha Beer Singh, Aditya Dev Mishra and T. Saravanan
19.1 Introduction 406
19.2 AGI and Quantum Computing 409
19.3 AGI and BCIs 413
19.4 The Development of General AI 418
19.5 Future Research Directions 423
19.6 Conclusion 425
Abbreviations 426
References 426
Index 429