Problem solving methods programming future concepts 1st Edition by German, Dimitri Ofitserov ISBN 0444544313 9780444544315

Problem solving is the very area of articifical intelligence AI which, probably, will never result in a complete set of formalized theories, in a pragmatic philosphy, or in a “universal” applied discipline. Studying questions concerning this area, encompasses different concepts, models and theories. This volume of the series looks at classifying problems, interpreting them, and the methods of solving them. The final chapter covers future concepts such as universal problem solving approach restoration, weak methods becoming strong, the role of formal logic in future developments, human factors and other paradigms. Different groups of readers such as mathematicians, specialists in computer sciences, and programmers will find this title of interest. Post-graduates and the students specializing in AI and applied mathematics will also find the work useful.

Problem solving methods programming future concepts 1st Edition Table of contents:

Part I: Problem-Solving Foundations

2. Approaching Problem Solving

   • Understanding the Problem: Analysis and Decomposition
   • Developing an Efficient Plan for Problem Solving
   • Identifying and Applying Relevant Algorithms
   • Case Study: Problem Solving in Real-World Applications

3. Methods and Strategies for Problem Solving

   • Top-Down and Bottom-Up Approaches
   • Divide and Conquer Strategy
   • Brute Force vs. Optimized Algorithms
   • Case Study: Comparing Approaches in Software Development

4. Algorithms and Data Structures

   • Essential Data Structures for Problem Solving
   • Choosing the Right Data Structure
   • Basic Sorting and Searching Algorithms
   • Case Study: Algorithm Selection in Data Processing

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Part II: Programming Techniques

5. Algorithm Design and Optimization

   • Key Concepts in Algorithm Design
   • Greedy Algorithms, Dynamic Programming, and Backtracking
   • Optimization Techniques: Minimizing Time and Space Complexity
   • Case Study: Optimizing Algorithms for Computational Efficiency

6. Programming Paradigms

   • Procedural Programming vs. Object-Oriented Programming
   • Functional Programming and Declarative Approaches
   • Logic Programming and Its Applications in Problem Solving
   • Case Study: Solving Complex Problems with Different Paradigms

7. Debugging and Testing Strategies

   • Common Debugging Techniques
   • Writing Testable Code and Unit Testing
   • Performance Profiling and Optimization
   • Case Study: Efficient Debugging in Large-Scale Projects

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Part III: Advanced Problem-Solving Concepts

8. Problem Solving with Artificial Intelligence

   • Introduction to AI and Its Role in Problem Solving
   • Search Algorithms and Heuristics
   • Machine Learning and Problem Solving
   • Case Study: AI Applications in Autonomous Systems

9. Parallel and Distributed Problem Solving

   • Parallel Programming Models and Techniques
   • Distributed Systems and Problem Solving at Scale
   • Case Study: Solving Big Data Problems with Parallelism

10. Solving Problems with Advanced Computational Models

    • Quantum Computing and Its Potential for Problem Solving
    • Neural Networks and Deep Learning in Complex Problem Solving
    • Case Study: Solving Optimization Problems with Machine Learning

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Part IV: The Future of Problem Solving in Computing

11. Future Trends in Problem Solving and Programming

    • The Evolution of Programming Languages
    • The Role of Artificial Intelligence in Shaping Future Programming
    • Predicting the Future of Algorithms and Problem-Solving Techniques
    • Case Study: The Future of Problem Solving in Autonomous Vehicles

12. The Role of Human Intelligence in Problem Solving

    • Cognitive Models of Problem Solving
    • Human-Machine Collaboration in Complex Problem Solving
    • Ethical Considerations in AI and Automation
    • Case Study: Human and AI Collaboration in Healthcare

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