Lecture-03-04-Uninformed Search

Lecture-03-04-Uninformed Search - CS 561: Artificial...

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CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, macskass@usc.edu TAs: Nadeesha Ranashinghe ( nadeeshr@usc.edu ) William Yeoh ( wyeoh@usc.edu ) Harris Chiu ( chiciu@usc.edu ) Lectures: MW 5:00-6:20pm, OHE 122 / DEN Office hours: By appointment Class page: http://www-rcf.usc.edu/~macskass/CS561-Spring2010/ This class will use http://www.uscden.net/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (2nd ed)
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Practical issues coding projects Programming projects should be done in C++ or C No java. CS561 - Lectures 3-4 - Macskassy - Spring 2010 2
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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 3 CS561 - Lectures 3-4 - Macskassy - Spring 2010
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Outline: Problem solving and search (Uninformed Search AIMA Ch. 3] Introduction to Problem Solving Complexity Uninformed search Problem formulation Search strategies: depth-first, breadth-first Informed search Search strategies: best-first, A* Heuristic functions 4 CS561 - Lectures 3-4 - Macskassy - Spring 2010
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Example: Measuring problem! Problem: Using these three buckets, measure 7 liters of water. 3 l 5 l 9 l 5 CS561 - Lectures 3-4 - Macskassy - Spring 2010
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Example: Measuring problem! (one possible) Solution: a b c 0 0 0 start 3 l 5 l 9 l a b c 6 CS561 - Lectures 3-4 - Macskassy - Spring 2010
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Example: Measuring problem! Another Solution: a b c 0 0 0 start 3 l 5 l 9 l a b c 7 CS561 - Lectures 3-4 - Macskassy - Spring 2010
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Which solution do we prefer? Solution 1: 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 Solution 2: a b c 0 0 0 start 0 5 0 3 2 0 3 0 2 3 5 2 3 0 7 goal 8 CS561 - Lectures 3-4 - Macskassy - Spring 2010
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Problem-Solving Agent function S IMPLE -P ROBLEM -S OLVING -A GENT ( percept ) returns an action static: seq , an action sequence, initially empty state , some description of the current world state goal , a goal, initially null problem , a problem formulation state U PDATE -S TATE ( state , percept ) if seq is empty then goal F ORMULATE -G OAL ( state ) problem F ORMULATE -P ROBLEM ( state , goal ) seq S EARCH ( problem ) action R ECOMMENDATION ( seq , state ) seq R EMAINDER ( seq , state ) return action CS561 - Lectures 3-4 - Macskassy - Spring 2010 9 Note: This is offline problem-solving.
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Lecture-03-04-Uninformed Search - CS 561: Artificial...

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