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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

  1. Restricted Domains and Formalization:

    • Games provide well-defined, structured environments with clear rules and objectives. This makes them ideal for experimentation and formalization.
    • Researchers can precisely define game states, legal moves, and winning conditions, allowing them to create algorithms that reason about these aspects.
    • Chess, checkers, and Go are classic examples. Go, in particular, presents immense complexity due to its vast branching factor and strategic depth.

  2. Algorithmic Challenges:

    • Games pose algorithmic challenges that require intelligent decision-making.
    • Search algorithms, such as depth-first search, breadth-first search, and heuristic-based approaches, are fundamental tools for game-playing AI.
    • These algorithms explore the game tree to find optimal or near-optimal moves, considering various possibilities.

  3. Minimax Algorithm:

    • Developed in the 1960s, the Minimax algorithm is a cornerstone of game-playing AI.
    • It’s used for adversarial games (where opponents compete), like chess and tic-tac-toe.
    • Minimax explores the game tree by alternating between maximizing and minimizing players. It assumes that opponents play optimally.
    • Alpha-Beta Pruning, an enhancement of Minimax, reduces the search space by pruning branches that won’t affect the final outcome.

  4. Planning and Heuristic Search:

    • Beyond games, planning problems involve finding sequences of actions to achieve goals.
    • Heuristic search algorithms, like A* (A-star), combine informed search with heuristics to efficiently explore state spaces.
    • These techniques have applications beyond games, such as robotics path planning, scheduling, and logistics.

  5. Benchmarking and Competition:

    • Games provide a standardized benchmark for evaluating AI performance.
    • Tournaments and competitions (e.g., the Computer Chess Championship) drive innovation and encourage researchers to develop better algorithms.
    • Success in games demonstrates progress in AI capabilities.

  6. Transferable Skills:

    • Techniques developed for game-playing AI often find applications in other domains.
    • For instance, search algorithms used in chess engines can be adapted for web search engines or route planning.

Games serve as both testbeds and inspiration for AI research. They challenge us to create intelligent agents that can reason, plan, and make optimal decisions within constrained environments. As AI continues to evolve, games remain a fascinating and fruitful area of study. 

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