ajay dev learning AI: Search Methods for Problem Solving

 Week 1: Introduction to AI and Search

• AI stands for Artificial Intelligence. It is the study of how to make computers think and learn like humans.
• The Turing Test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
• The Winograd Schema Challenge is a test of a machine's ability to understand and reason about natural language.
• Search algorithms are used to find solutions to problems. They work by exploring different possibilities and choosing the best one.

Week 2: Search Spaces and Algorithms

• A search space is the set of all possible solutions to a problem.
• State space search is a type of search algorithm that explores all possible states of a problem to find a solution.
• Depth-First Search (DFS) is a search algorithm that explores the search space by going as deep as possible first.
• Breadth-First Search (BFS) is a search algorithm that explores the search space by going as wide as possible first.
• Iterative Deepening Search (IDS) is a search algorithm that combines DFS and BFS to find solutions to problems with large search spaces.

Week 3: Heuristic Search

• A heuristic function is a function that estimates the distance between a current state and a goal state.
• Heuristic search algorithms use heuristic functions to guide their search through the search space.
• Stochastic local search methods are a type of heuristic search algorithm that uses random choices to explore the search space.

Week 4: Population-Based Methods

• Population-based optimization methods are a type of search algorithm that works with a population of solutions to find a good solution.
• Genetic Algorithms are a type of population-based optimization method that is inspired by the process of natural selection.
• Emergent systems are systems that arise from the interaction of many simple components. Ant Colony Optimization is a type of population-based optimization method that is inspired by the behavior of ants.

Week 5: Optimal Path Finding

• Optimal path-finding problems are problems where the goal is to find the shortest or most efficient path between two points.
• The A* algorithm is a search algorithm that is used to find optimal paths.
• Admissibility is a property of heuristic functions that ensures that the A* algorithm will always find an optimal solution.

Week 6: Monotone Conditions and Sequence Alignment

• The monotone condition is a property of heuristic functions that ensures that the A* algorithm will never explore the same state twice.
• Space-saving versions of A* are versions of the A* algorithm that use less memory.
• Sequence alignment is a technique that is used to compare two sequences and find their similarities. Search algorithms can be used to solve sequence alignment problems.

Week 7: Game Playing

• Game playing is a popular application of AI.
• The Minimax algorithm is a search algorithm that is used to find the best move to make in a game.
• Alpha-Beta pruning is a technique that is used to improve the performance of the Minimax algorithm.
• SSS* is a search algorithm that is specifically designed for game playing.

Week 8: Automated Planning

• Automated planning is the task of automatically generating a plan to achieve a goal.
• Goal Stack Planning and Partial Order Planning are two common automated planning algorithms.

Week 9: Problem Decomposition and AO Algorithm*

• Problem decomposition is the technique of breaking down a large problem into smaller, more manageable problems.
• Algorithm AO* is a search algorithm that is designed for problem solving and optimization.

Week 10: Pattern-Directed Inference Systems and Reasoning

• Pattern-directed inference systems are a type of AI system that uses patterns to reason about problems.
• Forward chaining inference engines are a type of pattern-directed inference system that works by applying rules to data.
• The Rete algorithm is a technique that is used to improve the performance of forward chaining inference engines.

Week 11: Constraint Processing and Model-Based Diagnosis

• Constraint processing is a technique for solving problems by imposing constraints on the solutions.
• The Backtracking algorithm is a constraint processing algorithm that can be used to solve a variety of problems.
• The Waltz algorithm is a technique that combines search and reasoning to solve constraint processing problems.
• Model-based diagnosis is the task of identifying the cause of a problem in a system. AI techniques can be used to implement model-based diagnosis systems.

I hope this simplified explanation is helpful. Please let me know if you have any other questions.Prescribed Books

The following are the suggested books for the course:

Deepak Khemani. A First Course in Artificial Intelligence, McGraw Hill Education (India), 2013. (Chapters 1 – 8, some parts from Chapters 9 and 10))

Reference

John Haugeland, Artificial Intelligence: The Very Idea, A Bradford Book, The MIT Press, 1985.

Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, A K Peters/CRC Press; 2nd  edition, 2004.

Eugene Charniak and Drew McDermott, Introduction to Artificial Intelligence, Addison- Wesley Publ., 1985.

Zbigniew Michalewicz and David B. Fogel. How to Solve It: Modern Heuristics. Springer; 2nd edition, 2004.

Judea Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving,  Addison-Wesley, 1984.

Elaine Rich and Kevin Knight. Artificial Intelligence, Tata McGraw Hill, 1991.

Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall, 2009.

Patrick Henry Winston. Artificial Intelligence, Addison-Wesley, 1992.

Stefan Edelkamp and Stefan Schroedl. Heuristic Search: Theory and Applications,  Morgan Kaufmann,  2011.