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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

A game tree is a fundamental concept in AI, particularly when dealing with games. It provides a structured representation of the possible moves and states in a game. Here are some key points about game trees:

  1. Representation:

    • In a game tree, each node represents a specific state of the game. For example, in tic-tac-toe, a node might represent a particular arrangement of Xs and Os on the board.
    • The root node of the tree corresponds to the initial state of the game (e.g., an empty tic-tac-toe board).
    • Each level of the tree corresponds to a player’s turn. For instance, the second level contains nodes representing the possible states resulting from the first player’s move.

  2. Children Nodes:

    • The nodes directly connected to a parent node are called its children.
    • In tic-tac-toe, the root node’s children represent the possible moves of the first player (either X or O).
    • Each of these children nodes, in turn, has its own set of children representing the opponent’s responses.

  3. Expanding the Tree:

    • The tree expands as we explore more game states. For example, if the first player places an X in the top-left corner, we create a child node representing that state.
    • The opponent’s moves (O’s) then lead to further child nodes, and the tree continues to grow.

  4. Terminal Nodes:

    • The game tree continues until we reach terminal nodes—states where the game ends.
    • In tic-tac-toe, terminal nodes occur when one player wins (gets a line of three) or when the board is full (resulting in a tie).

  5. Search Algorithms:

    • AI algorithms can traverse the game tree to find optimal moves.
    • Minimax is a common algorithm that evaluates each leaf node (terminal state) and assigns a value (win, loss, or draw).
    • Alpha-beta pruning optimizes the search by eliminating branches that won’t affect the final decision.

  6. Beyond Tic-Tac-Toe:

    • Game trees apply to various games, from chess and checkers to more complex games like Go.
    • The depth of the tree depends on the game’s complexity and the available computational resources.

Remember that game trees are essential not only for playing games but also for planning, decision-making, and optimization problems. They provide a structured way to explore possibilities and make informed choices.

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