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Artificial Intelligence and Searching

Learn about the diverse field of artificial intelligence, including problem solving, reasoning, and game playing. Discover how AI simulates thought and navigates search spaces to find optimal solutions. Join us to explore the fascinating world of AI!

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Artificial Intelligence and Searching

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  1. Artificial Intelligence and Searching CPSC 315 – Programming Studio Fall 2008 Project 2, Lecture 1 Adapted from slides of Yoonsuck Choe

  2. Artificial Intelligence • Long-standing computational goal • Turing test • Field of AI very diverse • “Strong” AI – trying to simulate thought itself • “Weak” AI – trying to make things that behave intelligently • Several different approaches used, topics studied • Sometimes grouped with other fields • Robotics • Computer Vision

  3. Topics in Artificial Intelligence • Problem solving • Reasoning • Theorem Proving • Planning • Learning • Knowledge Representation • Perception • Agent Behavior • Understanding brain function and development • Optimizing • etc.

  4. Game Playing and Search • Game playing a long-studied topic in AI • Seen as a proxy for how more complex reasoning can be developed • Search • Understanding the set of possible states, and finding the “best” state or the best path to a goal state, or some path to the goal state, etc. • “State” is the condition of the environment • e.g. in theorem proving, can be the state of things known • By applying known theorems, can expand the state, until reaching the goal theorem • Should be stored concisely

  5. Really BasicState Search Example • Given a=b,b=c,c=d, prove a=d. a=b, b=c, c=d a=b, b=c, c=d a=c a=b, b=c, c=d b=d a=b, b=c, c=d b=d, a=d

  6. Operators • Transition from one state to another • Fly from one city to another • Apply a theorem • Move a piece in a game • Add person to a meeting schedule • Operators and states are both usually limited by various rules • Can only fly certain routes • Only valid moves in game

  7. Search • Examine possible states, transitions to find goal state • Interesting problems are those too large to explore exhaustively • Uninformed search • Systematic strategy to explore options • Informed search • Use domain knowledge to limit search

  8. Game Playing • Abstract AI problem • Nice and challenging properties • Usually state can be clearly, concisely represented • Limited number of operations (but can still be large) • Unknown factor – account for opponent • Search space can be huge • Limit response based on time – forces making good “decisions” • e.g. Chess averages about 35 possible moves per turn, about 50 moves per player per game, or 35100 possible games. But, “only” 1040 possible board states.

  9. Types of games • Deterministic vs. random factor • Known state vs. hidden information

  10. Game Playing • In upcoming lectures, we will discuss some of the basic methods for performing search • Project will focus on a deterministic game with perfect information

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