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Problem Solving in Artificial Intelligence
Rating: 4.3 out of 5(61 ratings)
1,411 students

Problem Solving in Artificial Intelligence

Guide for AI Problem Solving
Created byDrUsha G
Last updated 11/2024
English
English [Auto],

What you'll learn

  • Explain the principles of problem-solving in AI, including search strategies, optimization, and heuristics.
  • Differentiate between informed and uninformed search techniques.
  • Identify and describe classic algorithms like Breadth-First Search, Depth-First Search, A*
  • Understand the application of constraint satisfaction problems (CSPs) and optimization methods.

Course content

1 section • 9 lectures • 1h 30m total length

  • Introduction to Artificial Intelligence5:18

    Explore the fundamentals of artificial intelligence, from intelligent agents and problem solving to searching methods like breadth-first and A* search, plus supervised, unsupervised and reinforcement learning, neural networks, and ethics.

  • AI Applications11:18

    Explore AI applications to enhance problem solving in artificial intelligence through practical strategies and real-world use cases.

  • Problem Solving Agents12:40

    Explore problem solving agents in artificial intelligence and their role in solving tasks in practice.

  • Search Algorithms7:39

    Explore how search algorithms solve problems in artificial intelligence, applying problem-solving strategies to navigate solution spaces efficiently.

  • Uninformed Search Algorithms8:54

    Investigate uninformed search algorithms within problem solving in artificial intelligence. Understand how these algorithms contribute to finding solutions in artificial intelligence challenges.

  • Local Search Algorithms10:31

    Explore local search algorithms for optimization in large spaces, including hill climbing, simulated annealing, local beam search, genetic algorithms, and tabu search, with state space, neighborhood, and objective function concepts.

  • Constraint Satisfaction Problems9:42

    Explore constraint satisfaction problems (CSPs), where you assign values to variables within domains to satisfy constraints and applying backtracking, constraint propagation, and heuristics to real-world tasks like scheduling and sudoku.

  • AI Uncertainty15:04

    Explore how uncertainty arises from incomplete data and ambiguity in ai, and how probabilistic and fuzzy methods, Bayesian networks, Monte Carlo, and mdps build robust, explainable ai for autonomous systems.

  • Bayseian Networks9:27

    Explore Bayesian networks, their components, and exact and approximate inference methods, including causal networks, with real-time applications in medicine, weather, robotics, and fraud detection.

  • Quiz Questions in AI
  • Quiz2
  • Quiz 3
  • Quiz 4
  • Quiz5

Requirements

  • No prerequisites needed. This course will teach you the basics of problem solving techniques in Artificial Intelligence

Description

This course will teach you about the basic problem-solving and search algorithms used in Artificial Intelligence (AI). They will learn how to model difficult problems and use uninformed and informed search methods to find good solutions. Uninformed search methods, like Breadth-First Search and Depth-First Search, will be presented as organized ways to look into problem spaces without knowing much about them beforehand. On the other hand, smart search algorithms like A* and Greedy Best-First Search will show how rules can help people solve problems quickly. Constraint Satisfaction Problems (CSPs) are also covered in the course. Students learn to use variables, domains, and constraints to model and answer real-world problems. Methods like backtracking, forward checking, and heuristic ordering will be discussed to improve answers. Students will work on real-world problems like pathfinding, scheduling, and optimization while learning how to judge the success of an algorithm in terms of how complete, optimal, and efficient it is. Students will be able to formalize problems, choose the right algorithms, and put AI-based answers into action by the end of the course. This class is great for people who want to learn a lot about AI problem-solving, which is used in robotics, game creation, and systems that make decisions.

Who this course is for:

  • UG students, PG students, Phd students and Aspirants who want to know regarding Artificial Intelligence

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