Lecture Notes in Artificial Intelligence 1st edition by Acar Savaci ISBN 3540367136 978-3540367130

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ISBN 10: 3540367136
ISBN 13:  978-3540367130
Author: Acar Savaci

The Turkish Artificial Intelligence and Neural Network Symposium (TAINN) is an annual meeting where scientists present their new ideas and algorithms on artificial intelligence and neural networks with either oral or poster presentation. The TAINN- Turkish Conference on AI and NN Series started in 1992 at Bilkent University in Ankara, envisioned by various researchers in AI and NN then at the Bilkent, Middle East Technical, Bo aziçi and Ege universities as a forum for local researchers to get together and communicate. Since then, TAINN has been held annually around early summer. This year the 14th TAINN conference was organized by the EE and CE departments of the zmir Institute of Technology with an emphasis on international contributions. Among the 75 papers, 41 were accepted for oral presentation and 12 for poster presen-tation. In addition to the presentations, invited lectures were given by Burhan Türk en (Knowledge/Intelligence Systems Laboratory, University of Toronto) and Joerg Siekmann (German Research Center for Artificial Intelligence DFKI, Saarland University), providing us with new developments in the field. We are very grateful to the contributions of such pioneer scientists.

Lecture Notes in Artificial Intelligence 1st Table of contents:

Preface

• Introduction to the Series
• Overview of Topics Covered in Volume 1
• Acknowledgments

Chapter 1: Artificial Intelligence: An Introduction

• What is Artificial Intelligence?
• History and Evolution of AI
• Major Challenges in AI
• Applications of AI in Various Fields

Chapter 2: Knowledge Representation and Reasoning

• The Role of Knowledge Representation in AI
• Symbolic vs. Subsymbolic Representation
• Logic-Based Reasoning Systems
• Reasoning with Uncertainty

Chapter 3: Search Algorithms and Problem Solving

• Classical Search Algorithms (e.g., A*, BFS, DFS)
• Heuristic Search Methods
• Optimization Problems in AI
• Real-Time and Anytime Search Algorithms

Chapter 4: Machine Learning and Data Mining

• Basics of Machine Learning
• Supervised vs. Unsupervised Learning
• Decision Trees, Neural Networks, and Support Vector Machines
• Data Mining Techniques in AI

Chapter 5: Natural Language Processing (NLP)

• Language Models and Syntax
• Semantic Representation and Understanding
• NLP Techniques in Speech Recognition and Translation
• Challenges in NLP and AI

Chapter 6: Planning and Decision Making

• Automated Planning Algorithms
• Markov Decision Processes (MDPs)
• Decision Theory and Reinforcement Learning
• Multi-Agent Systems and Cooperation

Chapter 7: Robotics and Perception

• Basics of Robotics in AI
• Sensors and Perception Systems
• Autonomous Robots and Machine Vision
• Real-World Challenges in Robotics

Chapter 8: Neural Networks and Deep Learning

• Fundamentals of Artificial Neural Networks
• Deep Learning Architectures (e.g., CNNs, RNNs)
• Backpropagation and Training Algorithms
• Applications of Deep Learning

Chapter 9: Ethical and Societal Impacts of AI

• Ethical Considerations in AI Development
• Privacy and Security Issues
• Bias and Fairness in AI Algorithms
• The Future of AI and its Impact on Society

Chapter 10: Future Directions and Open Problems

• Emerging Trends in AI Research
• Challenges for the Next Generation of AI
• AI in Interdisciplinary Domains (e.g., Bioinformatics, Climate Modeling)
• Conclusion and Future Prospects

Index

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