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Course Content
Searching & Solving The Problems.
In the context of artificial intelligence (AI) and problem-solving, the concept of search plays a crucial role. Let's break it down: 1. Problem Formulation: - When we encounter a problem, we first need to define it in a way that allows us to search for a solution. - This involves identifying: - State Space: The set of possible configurations or states relevant to the problem. - Initial State: The starting point. - Goal State: The desired outcome. - Actions: The available moves or transitions between states. - Transition Model: Describes how actions lead from one state to another. 2. Search Algorithms: - Once we have the problem formulated, we apply search algorithms to explore the state space and find a path from the initial state to the goal state. - Common search algorithms include: - Depth-First Search (DFS): Explores as far as possible along a branch before backtracking. - Breadth-First Search (BFS): Explores all neighbors of the current state before moving to the next level. - A Search: Combines information about both the cost to reach a state and an estimate of the remaining cost to the goal. 3. Heuristics and Optimization: - Heuristics guide the search by providing estimates of how promising a state is. - Optimization involves finding the best solution based on some criteria (e.g., minimizing cost or maximizing utility). 4. Applications: - Search algorithms are used in various AI applications: - Game Playing: Finding optimal moves in games like chess or Go. - Route Planning: Navigating maps or finding the shortest path. - Constraint Satisfaction Problems: Solving puzzles or scheduling tasks. - Natural Language Processing: Searching for relevant documents or answers. 5. Challenges: - Complexity: Some problems have vast state spaces, making exhaustive search impractical. - Informed Search: Balancing exploration (finding new states) and exploitation (focusing on promising states). - Adversarial Environments: Dealing with opponents who actively try to thwart our goals.
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Search and Planning Problems
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Chicken Crossing Puzzle
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The States based on the given constraints.
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The State Space
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The Concept of Transitions
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Costs
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Problem Solving with Artificial Intelligence
1. Understanding Problem Solving in AI: - Definition: Problem solving in AI involves using various algorithms and models designed to mimic human cognitive processes. - Process: These algorithms analyze data, generate potential solutions, and evaluate the best course of action⁴. - Adaptability: AI systems need to be adaptive, learn from experiences, and make decisions even in uncertain conditions². 2. Foundations of AI Problem-Solving: - Components: - Problems: The core challenges that need solutions. - Problem Spaces: The vast and intricate domains where solutions reside. - Search Algorithms: Crucial for efficiently navigating problem spaces and finding optimal or near-optimal answers³. - Goal: Efficiently find solutions by systematically exploring possible actions. 3. Choosing the Right AI Approach: - Organizations should consider a range of analytics tools, not just generative AI. - Leaders must ask: - Which analytics tool fits the specific problem? - How to avoid choosing the wrong one? - Collaboration with technical experts ensures using the right tool for the job, building a foundation for future innovations¹.
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History Of AI
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Turing Machines & WWII.
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Games & Search Algorithms
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Search & Games
Max's pessimism likely stems from the fact that Min had just played her turn, and the board was set up for her to win with three Os in the top row. Max must find a way to block Min's winning move and secure her own victory. The top row is a critical position, and Max needs to strategize carefully!
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A Game Tree
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Min-Max algorithm
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A Sample Game Tree
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Strategic Mode
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Reasoning for determining the Winner.
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Game Theory
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The Minimax Algorithm
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The 1996 Match
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Solving Problems With AI
About Lesson

The match between Garry Kasparov and IBM’s Deep Blue in 1997 was a pivotal moment in the history of artificial intelligence and chess. Let’s delve into the details:

  1. The 1996 Match:

    • In 1996, the first match took place in Philadelphia. Kasparov, the reigning world chess champion, faced off against Deep Blue.
    • The six-game match resulted in a victory for Kasparov, with a score of 4–2.
    • Notably, this was the first time a reigning world champion had ever lost to a computer under tournament conditions and slow time controls.

  2. The 1997 Rematch:

    • The much-anticipated rematch occurred in New York City in 1997.
    • Deep Blue had undergone improvements, aiming to prove the rapid progress of artificial intelligence.
    • The rematch consisted of six games.
    • Deep Blue emerged victorious, winning the match with a score of 3½–2½.
    • This rematch marked the first defeat of a reigning world chess champion by a computer under tournament conditions.
    • A documentary film titled “Game Over: Kasparov and the Machine” captured the intensity of this battle.

  3. Heuristic Evaluation Function:

    • To manage massive game trees, Deep Blue used a heuristic evaluation function.
    • At each node in the game tree, the heuristic evaluated the board position and estimated the likely outcome.
    • The depth-limited version of the minimax algorithm returned this heuristic score at all nodes up to a specified depth limit.
    • By combining search algorithms with heuristics, Deep Blue made informed decisions while exploring only a fraction of the game tree.

The clash between Kasparov and Deep Blue showcased the power of AI in chess and left an indelible mark on the intersection of man and machine in competitive games.

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