The "AI Problem-Solving and Logic" course delves into how artificial intelligence in problem solving tackles complex challenges through logical reasoning, search algorithms, and decision-making frameworks.
Artificial Intelligence in Problem Solving
The "AI Problem-Solving and Logic" course delves into how artificial intelligence in problem solving tackles complex challenges through logical reasoning, search algorithms, and decision-making frameworks. By focusing on core AI techniques, this course explores how artificial intelligence in problem solving has evolved to address real-world issues efficiently. Students will gain a deep understanding of AI methodologies, including heuristic approaches, constraint satisfaction problems, and planning systems that optimize solutions.
This course provides both theoretical knowledge and practical applications to demonstrate how artificial intelligence and problem solving interact in structured and uncertain environments. Learners will explore knowledge representation, search techniques, and reasoning processes that allow AI systems to deliver logical and optimal outcomes. By the end of the course, participants will be well-prepared to implement AI strategies for solving diverse problems across industries.
This course is ideal for: Students and professionals seeking a strong foundation in AI problem-solving techniques. AI enthusiasts and data scientists interested in improving their logical reasoning and decision-making skills. Engineers, developers, and researchers working on AI models for real-world problem solving. Individuals aiming to explore the role of logic, algorithms, and heuristics in AI systems. No prior AI experience is required, but basic programming knowledge (e.g., Python) will be beneficial for hands-on exercises.
Understand the foundational principles of artificial intelligence and problem solving.
Apply search algorithms to solve structured and unstructured problems.
Utilize constraint satisfaction techniques for logical problem-solving.
Implement logical reasoning and knowledge representation in AI systems.
Optimize problem-solving efficiency using heuristic approaches.
Design AI planning and decision-making models for complex challenges.
Develop strategies to address uncertainty in AI-driven environments.
Analyze and evaluate AI methods for real-world problem-solving scenarios.
-
Explore how AI tackles complex problems using logical structures and algorithms. Understand the significance of artificial intelligence in problem solving and its real-world applications.
-
Learn various search algorithms, including breadth-first, depth-first, and A* search, to optimize problem-solving processes and discover efficient solutions.
-
Understand CSPs and how AI systems approach problems with multiple constraints, using techniques such as backtracking and forward checking.
-
Dive into propositional and first-order logic, exploring how AI uses logical reasoning for problem-solving and decision-making.
-
Discover methods for representing knowledge in AI systems and how reasoning mechanisms derive conclusions to solve problems.
-
Learn how heuristic techniques improve problem-solving efficiency by reducing computational costs and optimizing solutions.
-
Explore AI planning techniques, including STRIPS and hierarchical planning, for decision-making in complex environments.
-
Address challenges in uncertain environments using probabilistic models, Markov decision processes (MDPs), and reinforcement learning techniques.
Earn a Professional Certificate
Earn a certificate of completion issued by Learn Artificial Intelligence (LAI), recognised for demonstrating personal and professional development.
Aamir
TestTest
Aamir
TestTest
Yes, this course starts with foundational concepts of AI and problem-solving before advancing to more complex techniques. Beginners will find it accessible, while advanced learners can deepen their skills.
The course primarily uses Python along with AI tools and libraries like NumPy, TensorFlow, and other problem-solving frameworks. Instructions are provided to help learners get started.
Yes, the course includes practical exercises, coding tasks, and real-world case studies to reinforce learning through application.
AI uses algorithms, logic, and data-driven techniques to analyze problems, identify solutions, and automate decision-making processes. Examples include route optimization, scheduling, and medical diagnosis.
Logic in AI allows systems to reason, deduce conclusions, and make decisions based on predefined rules and relationships between data elements. It forms the foundation for knowledge representation and reasoning.
The four main problems AI can solve include: • Search and optimization problems • Constraint satisfaction problems (CSPs) • Uncertainty and probabilistic decision-making • Logical reasoning and inference tasks
Key Aspects of Course
CPD Accredited
Recognized for Professional Growth
Flexible & 24/7 Access
Learn anytime , anywhere
