mychapter4a - Local Search CHAPTER 4, Part II Oliver...

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CHAPTER 4, Part II Oliver Schulte Summer 2011 Local Search
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Outline Hill-Climbing Gradient Descent 2nd-order methods.
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Environment Type Discussed In this Lecture Static Environment CMPT 310 - Blind Search 3 Fully Observable Deterministic Sequential yes yes Discrete Discrete yes Planning, heuristic search yes Control, cybernetics no no Continuous Function Optimization Vector Search: Constraint Satisfaction no yes
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Optimization Problems An optimization problem is of the form maximize f( x ) subject to constraints on  x Equivalently   minimize -f( x ) subject to constraints on  x If the constraints are linear (in)equalities, we have  linear programming  problem.  Very large literature built up over centuries.
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AI examples Find the right angle of joints for pancake- flipping robots. Find the best weights for rules for reasoning. Basically, any problem with continuous variables at some point needs optimization to build the best agent.
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Example:  n -queens Put  n  queens on an  n × n  board with no two  queens on the same row, column, or diagonal Demo for n-Queens Hill-Climbing
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Hill-climbing search Random-restart hill climbing over comes local maxima- Trivially 
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This note was uploaded on 07/04/2011 for the course CMPT 310 taught by Professor Oliver during the Summer '11 term at Simon Fraser.

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mychapter4a - Local Search CHAPTER 4, Part II Oliver...

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