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session03-05

# session03-05 - Last time Summary Definition of AI Turing...

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CS 561, Lectures 3-5 1 Last time: Summary Definition of AI? Turing Test? Intelligent Agents: Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through its effectors to maximize progress towards its goals . PAGE (Percepts, Actions, Goals, Environment) Described as a Perception (sequence) to Action Mapping: f : P * A Using look-up-table, closed form, etc. Agent Types: Reflex, state-based, goal-based, utility-based Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence to date

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CS 561, Lectures 3-5 2 Outline: Problem solving and search Introduction to Problem Solving Complexity Uninformed search Problem formulation Search strategies: depth-first, breadth-first Informed search Search strategies: best-first, A* Heuristic functions
CS 561, Lectures 3-5 3 Example: Measuring problem! Problem: Using these three buckets, measure 7 liters of water. 3 l 5 l 9 l

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CS 561, Lectures 3-5 4 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 5 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 l 5 l 9 l a b c

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CS 561, Lectures 3-5 6 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 7 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 0 3 3 l 5 l 9 l a b c

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CS 561, Lectures 3-5 8 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 0 3 0 0 6 3 0 6 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 9 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 0 3 0 0 6 3 0 6 3 l 5 l 9 l a b c

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CS 561, Lectures 3-5 10 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 0 3 0 0 6 3 0 6 0 3 6 3 3 6 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 11 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 0 3 0 0 6 3 0 6 0 3 6 3 3 6 3 l 5 l 9 l a b c

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CS 561, Lectures 3-5 12 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 0 3 0 0 6 3 0 6 0 3 6 3 3 6 1 5 6 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 13 Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 0 0 0 0 3 3 0 3 0 0 6 3 0 6 0 3 6 3 3 6 1 5 6 0 5 7 goal 3 l 5 l 9 l a b c

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CS 561, Lectures 3-5 14 Example: Measuring problem! Another Solution: a b c 0 0 0 start 0 5 0 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 15 Example: Measuring problem! Another Solution: a b c 0 0 0 start 0 5 0 3 2 0 3 l 5 l 9 l a b c

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CS 561, Lectures 3-5 16 Example: Measuring problem! Another Solution: a b c 0 0 0 start 0 5 0 3 2 0 3 0 2 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 17 Example: Measuring problem! Another Solution: a b c 0 0 0 start 0 5 0 3 2 0 3 0 2 3 5 2 0 0 6 3 l 5 l 9 l a b c

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CS 561, Lectures 3-5 18 Example: Measuring problem! Another Solution: a b c 0 0 0 start 0 5 0 3 2 0 3 0 2 3 5 2 3 0 7 goal 3 0 6 3 l 5 l 9 l a b c
CS 561, Lectures 3-5 19 Which solution do we prefer?

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session03-05 - Last time Summary Definition of AI Turing...

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