Lecture 16 memory bounded search

Lecture 16 memory bounded search

• 1.
  Memory Bounded Search RecursiveBest First Search(Extensions of BFS) Lecture-16 Hema Kashyap 1
• 2.
  Memory Bounded Search •RBFS(Recursive Best First Search) • IDA* (Iterative Deepening A* Search) – Is a logical extension of ITERATIVE –DEEPENING SEARCH to use heuristic information • SMA*(Simplified Memory Bound A*) Search 2
• 3.
  RBFS-Properties • Similar toA* algorithm developed for heuristic search – Both are recursive in the same sense • Difference between A* and RBFS – A* keeps in memory all of the already generated nodes – RBFS only keeps the current search path and the sibling nodes along the path 3
• 4.
  • When doesRBFS suspend the search of a subtree? – When it no longer looks the best • What does it do when a subtree is suspended? – It forgets the subtree to save space • What is the space complexity? – Linear the depth of the search – Same as IDA* 4
• 5.
  F value Inheritance •F-values can be inherited from a nodeʼs parents • Let N be a node about to be expanded – If F(N) > f(N) then N had already been expanded – F(N) was determined from Nʼs children – Children have been removed from memory • Suppose a child Nk of N is generated again – Compute f(Nk) – F(Nk) = max ( F(N) , f(Nk) ) • Nk ʼs F-value can be inherited from N – Nk was generated earlier – F(Nk) was ≥ F(N), otherwise F(N) would be smaller 5
• 6.
  • RBFS usesonly linear space. • It mimics best first search. • It keeps track of the f-value of the best alternative path available from any ancestor of the current node. • If the current node exceeds this limit, the alternative path is explored. • RBFS remembers the f-value of the best leaf in the forgotten sub-tree. 6
• 7.
  RBFS-Algorithm function RECURSIVE-BEST-FIRST-SEARCH(problem) returnsa solution, or failure return RBFS(problem,MAKE-NODE(problem.INITIAL-STATE),∞) function RBFS(problem, node, f limit ) returns a solution, or failure and a new f-cost limit if problem.GOAL-TEST(node.STATE) then return SOLUTION(node) successors ←[ ] for each action in problem.ACTIONS(node.STATE) do add CHILD-NODE(problem, node, action) intosuccessors if successors is empty then return failure,∞ for each s in successors do /* update f with value from previous search, if any */ s.f ←max(s.g + s.h, node.f )) loop do best ←the lowest f-value node in successors if best .f > f limit then return failure, best .f alternative ←the second-lowest f-value among successors result , best .f ←RBFS(problem, best , min( f limit, alternative)) if result = failure then return result 7
• 8.
  Example 8
• 9.
  H-values 9
• 10.
  Example 10
• 11.
  Example 11
• 12.
  Example 12