Grundlagen der künstlichen Intelligenz at TU München

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Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What are Heuristics in general?

Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What is the basic idea behind Greedy Best-First Search?

Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What is the performance of Greedy Best-First Search?

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Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What is the idea behind A* search?

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What is an admissible heuristic?

Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What is a consistent/monotonic heuristic? When is it required?

Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What is the effect of the heuristic in A* Search?

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Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What is pruning in A* Search?

Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

What is the performance of A* Search?

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Which alternatives to A* Search did we see? What is a disadvantage of A*?

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How do we characterize the quality of a heuristic?

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How does informed search work? Which constraints does a heuristic function have?

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Exemplary flashcards for Grundlagen der künstlichen Intelligenz at the TU München on StudySmarter:

Grundlagen der künstlichen Intelligenz

What are Heuristics in general?

  • refer to art of achieving good solutions with limited knowledge and time based on experience

Grundlagen der künstlichen Intelligenz

What is the basic idea behind Greedy Best-First Search?

  • expands node that is closest to goal by using just heuristic function so that f(n) = h(n)

Grundlagen der künstlichen Intelligenz

What is the performance of Greedy Best-First Search?

  • Completeness: Yes, if graph search is used, otherwise no
  • Optimality: No
  • Time complexity: worst case is that heuristic is misleading to search such that solution is found last O(b^m); good heuristic can improve
  • Space complexity: equals time complexity since all nodes are stored: O(b^m)

Grundlagen der künstlichen Intelligenz

What is the idea behind A* search?

  • evaluates nodes by combining path cost g(n) and estimated cost to goal h(n): f(n) = g(n) + h(n)
  • h(n) has to be admissible (underestimation)
    • f(n) never overestimates cost of goal
    • algorithm keeps searching for paths that might have lower cost to goal than those found prevoiusly

Grundlagen der künstlichen Intelligenz

What is an admissible heuristic?

  • Underestimation, has to be less than actual cost

Grundlagen der künstlichen Intelligenz

What is a consistent/monotonic heuristic? When is it required?

  • for given cost of transactions c(n,a,n') , we have that for all nodes n and its successors n':
    • h(n) <= c(n,a,n') + h(n')
  • every consistent heuristic is admissible
  • required for applying A* with graph search

Grundlagen der künstlichen Intelligenz

What is the effect of the heuristic in A* Search?

  • Heuristic "steers" search towards goal
  • A* expands nodes in order of increasing f value, so that "f-contours" of nodes are gradually added
  • Each contour i includes all nodes with f <= fi where fi < fi+1

Grundlagen der künstlichen Intelligenz

What is pruning in A* Search?

  • Given cost C* of optimal path, only paths with f(n) <= C* are expanded
    • equality occurs when heuristic returns exact remaining costs
  • Pruning = eliminating possibilities from consideration without having to examine them
    • brings enormous time savings

Grundlagen der künstlichen Intelligenz

What is the performance of A* Search?

  • Completeness: Yes, if costs are greater than 0 (otherwise infinite optimal paths of zero cost exist)
  • Optimality: Yes (if cost positive); heuristic has to be admissible for tree-search version and consistent for graph-search version
  • Time complexity: only consider easiest case: State space has single goal and all actions are reversible: O(b^(e*d)
    • relative error e = (h*-h)/h*
    • h is estimated and h* actual cost from root to goal
  • Space complexity: same as time complexity since all nodes are stored

Grundlagen der künstlichen Intelligenz

Which alternatives to A* Search did we see? What is a disadvantage of A*?

  • Disadvantage: huge space consumption possible
  • Iterative-deepening A* : adapts idea of iterative deepening to A*; main difference is that f-cost (g+h) is used for cutoff rather than depth
  • Recursive best-first search: simple recursive algorithm with linear space complexity; Structure is similar to recursive depth-first-search, but keeps track of f-value of best alternative path
  • Memory-bounded A* and simplified memory-bounded A*: These algorithms work just like A* until memory is full; drop less promising paths to free memory

Grundlagen der künstlichen Intelligenz

How do we characterize the quality of a heuristic?

  • effective branching factor b*
  • Given:
    • Number of nodes N generated by A* search
    • Uniform tree with depths d (each node has same fractional # b* of children)
  • N+1 = 1 + b* + b*^2 + ... + (b*)^d
  • makes it possible to compare heuristic applied to problems of different size

Grundlagen der künstlichen Intelligenz

How does informed search work? Which constraints does a heuristic function have?

  • uses indications how promising state is to reach a goal
  • can find solutions more efficiently than uninformed search
  • choice of next node is based on evaluation function f(n) (often called heuristic function h(n))
    • h(n) is problem specific with only constraint that it is nonnegative and h(^n) = 0 where ^n is goal node

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