Introduction to AI Concepts and Techniques

Sep 19, 2024

Introduction to Artificial Intelligence with Python

Overview of AI

  • Definition: AI encompasses techniques that allow computers to perform tasks that typically require human intelligence.
  • Examples:
    • Facial recognition
    • Game playing (e.g., chess, tic-tac-toe)
    • Natural language processing (e.g., voice recognition)

Key Topics Covered in the Course

  1. Search

    • How AI searches for solutions (e.g., driving directions, game moves)
    • Search problems can take various forms, like puzzles or mazes.

  2. Knowledge Representation

    • How AI can store and infer information
    • Representing knowledge and drawing conclusions.

  3. Uncertainty

    • Dealing with uncertainties and probabilities in AI.
    • How AI computes probability when facts are uncertain.

  4. Optimization

    • How AI optimizes for certain goals amidst multiple solutions.

  5. Machine Learning

    • AI's ability to learn from data and improve over time.
    • Examples include spam detection in emails.

  6. Neural Networks

    • Structure of neural networks inspired by the human brain to solve problems effectively.

  7. Natural Language Processing

    • Understanding human languages and the challenges that arise in AI interactions.

Focus on Search Problems

  • Search Problems Defined:
    • AI seeks solutions from an initial state to a goal state through actions.
    • Examples:
      • 15-puzzle
      • Finding a path through a maze.

Important Terminology

  • Agent: An entity that perceives its environment and acts upon it.
  • State: Configuration of the agent in its environment.
  • Initial State: The starting configuration for the search.
  • Actions: Possible moves available to the agent in a given state.
  • Transition Model: Describes how the state changes in response to an action.
  • Goal Test: Determines if the current state is a goal state.
  • Path Cost: Numerical cost associated with any given path.

Search Algorithms

  1. Depth-First Search (DFS)

    • Explores the deepest node in the frontier.
    • Pros: Less memory, simpler implementation.
    • Cons: May not find the optimal solution, may get stuck in loops.

  2. Breadth-First Search (BFS)

    • Explores the shallowest node in the frontier.
    • Pros: Guaranteed to find the optimal solution.
    • Cons: Requires more memory, may explore unnecessary states.

Example Comparison

  • Both DFS and BFS are applied to the same problem to contrast efficiency and solution optimality.

Adversarial Search

  • Minimax Algorithm: Used in games where two players have opposing objectives.
    • Max Player: Tries to maximize their score.
    • Min Player: Tries to minimize the opponent's score.
  • Alpha-Beta Pruning: An optimization technique for Minimax to disregard branches that won't yield better solutions.

Conclusion

  • The course will address knowledge representation and reasoning in AI in the next session.