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Chapter 4  Informed Search and Exploration
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Informed search strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Greedy best-first search ,[object Object],[object Object]
Greedy best-first search example
Properties of Greedy best-first search  ,[object Object],[object Object],[object Object],[object Object],No No  – can get stuck in loops, e.g.,  Iasi  –>  Neamt   – >  Iasi   – >  Neamt Yes  – complete in finite states with repeated-state checking , but a good heuristic function can give dramatic improvement –  keeps all nodes in memory
A* search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
:  Straight line distance  heuristic
A* search example
Optimality of A* ,[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of A* ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Memory-bounded heuristic search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
:  Straight line distance  heuristic
RBFS example
Memory-bounded heuristic search (cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
(Admissible) Heuristic Functions h 1 ?  h 2 ?  = the number of misplaced tiles = total  Manhattan  (city block) distance = 7 tiles are out of position = 4+0+3+3+1+0+2+1 = 14
Effect of heuristic accuracy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Inventing admissible heuristic functions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Local search algorithms and optimization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Local search – example
Local search – state space landscape ,[object Object],global minimum heuristic cost function ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],Hill-climbing search
[object Object],[object Object],[object Object],Hill-climbing search - example best moves reduce  h  = 17 to  h  = 12 local minimum with  h  = 1
Hill-climbing search – greedy local search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hill-climbing search – improvement ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simulated annealing search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simulated annealing search - Implementation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Local beam search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genetic algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genetic algorithms (cont’d) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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