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Artificial Intelligence and Searching

Artificial Intelligence and Searching. CPSC 315 – Programming Studio Fall 2008 Project 2, Lecture 1. Adapted from slides of Yoonsuck Choe. Artificial Intelligence. Long-standing computational goal Turing test Field of AI very diverse “Strong” AI – trying to simulate thought itself

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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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