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This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License . CS 312: Algorithm Analysis. Lecture #36: Best-first State-space Search: A*. Slides by: Eric Ringger, adapted from slides by Stuart Russell of UC Berkeley. Announcements. Homework #26 optional

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Cs 312 algorithm analysis

This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.

CS 312: Algorithm Analysis

Lecture #36: Best-first State-space Search: A*

Slides by: Eric Ringger, adapted from slides by Stuart Russell of UC Berkeley.


Announcements
Announcements

  • Homework #26 optional

  • Homework #27 due Wednesday

  • Project #7: TSP

    • Early: Wednesday

    • Due: Friday

    • Respond to survey by end of day Friday

    • Questions?

  • Will accept late project submissions up through next Tuesday at midnight!





Objectives
Objectives

  • Understand Best-first search as a sub-class of state-space search algorithms

  • Introduce A* Search

  • Compare and contrast with Branch & Bound

  • Understand Admissible Heuristics


Acknowledgment
Acknowledgment

  • Russell & Norvig: 4.1-4.2


Review state space search
Review: State-Space Search

  • Basic idea:

    • Represent partial solutions (and solutions) as states

    • Find a solution by searching the state space

    • Search involves generating successors of states (i.e., “expanding” states) and pruning wisely

  • A search strategy is defined by specifying the following:

    • The manner of state expansion

    • The order of state expansion


Uninformed search strategies
Uninformed search strategies

  • Use only the information available in the problem definition

  • Are not informed by discoveries during the search.

  • Examples:

    • Breadth-first search

    • Depth-first search

    • Depth-limited search

    • Iterative deepening search


Informed best first search
Informed: Best-first search

  • A type of informed search strategy

  • Use an evaluation functionf(n) for each state

    • Estimate of "desirability"

  • Basic idea: Explore (expand) most desirable / promising state, according to the evaluation function

  • Examples:

    • Without agenda:

      • Greedy best-first search

    • With agenda:

      • Uniform-cost search

        • Single goal, State-space variant of Dijkstra’s

      • A* search



Problem shortest path
Problem: Shortest Path

Admittedly, not a hard problem.

Map of Romania, distances in km


Greedy best first search
Greedy best-first search

  • Evaluation function (heuristic)

    = estimate of cost from state to

  • e.g., straight-line distance from to Bucharest

  • Greedy best-first search expands the state that appears to be closest to goal

  • No agenda

  • Contrast with Simple Scissors (from proj. #4)






Analysis of search strategies
Analysis of Search Strategies

  • We analyze search strategies in the following terms:

    • completeness: does it always find a solution if one exists?

    • optimality: does it always find an optimal solution, guaranteed?

    • time efficiency: number of states generated

    • space efficiency: maximum number of states in memory

      • “high water” mark on the agenda

  • Time and space efficiency are measured in terms of

    • b: maximum branching factor of the search tree

    • d: depth of the least-cost solution

    • m: maximum depth of the state space (may be ∞)


Properties of greedy best first search
Properties of greedy best-first search

  • Complete?

    • No – can get stuck in loops, e.g., Iasi Neamt Iasi Neamt …

      • or in dead ends

  • Optimal?

    • No

  • Time?

    • O(m*b)

  • Space?

    • O(m)


A search
A* search

  • Idea: avoid expanding paths that are already too expensive (at best)

  • Very similar to Branch and Bound!

    • But no BSSF

    • And no eager update of the BSSF

    • Solutions just go onto the agenda (always a priority queue)

    • First solution settled from the agenda wins

  • Also: Split the bound function into two parts:

    • Evaluation function f(n) = g(n) + h(n)

      = estimated total cost of path through n to goal

    • g(n) = cost so far to reach state n

    • h(n) = estimated cost to go from state n to goal


A search example
A* search example


A search example1
A* search example


A search example2
A* search example


A search example3
A* search example


A search example4
A* search example


A search example5
A* search example

Contrast with Dijkstra’s.

Contrast with B&B.


Properties of a
Properties of A*

  • Complete?

    • Yes

      • unless there are infinitely many states n such that f(n) ≤ f(G)

  • Optimal?

    • Yes, given an admissible heuristic

  • Time?

    • O(bm), Exponential

  • Space?

    • O(bm), worst case: keeps all states on agenda


For comparison uniform cost search
For Comparison:Uniform Cost Search

  • How does A* compare with Dijkstra’s algorithm?

    • Both use priority queue as agenda

    • A* solved shortest path (singular); Dijkstra’s solves shortest paths (plural)

    • A* has better heuristic function; Dijkstra’s: h(n)=0

      • 0 is uniformly the estimated remaining cost for every state

    • A* searches graph or state-space; Dijkstra’s explores a graph

  • In state-space search: Uniform cost search is the single-goal version of Dijkstra’s

  • How could you have used A* in project #4?

  • What about project #5?


Admissible heuristics
Admissible heuristics

  • A heuristic h(n) is admissible iff for every state n,

    h(n) ≤ h*(n)

    • where h*(n) is the true cost to reach the goal state from n.

  • An admissible heuristic never overestimates the cost to reach the goal

    • it is optimistic

    • it is a lower bound for minimization

    • (upper bound for maximization)

  • Example: hSLD(n) for the shortest path problem

    • never overestimates the actual road distance

  • Theorem: If h(n) is admissible, then A* is optimal


For project 7
For Project #7

  • Would you use A* for Project #7?

  • Why not?

  • What happens if you use a simple priority queue for your agenda in B&B?

  • Would it be correct?

  • Will you win?


For comparison branch and bound optimization
For comparison:Branch and Bound Optimization

  • Not best-first!

  • Idea: avoid expanding paths that are already too expensive at best, and prune them!

  • BSSF = best solution so far

    • Allows pruning

    • Permits any-time solution

  • Bound function f(n)

    • Estimated total cost of path through state nto goal (an optimistic bound)

  • You know this well!


Properties of branch and bound
Properties of Branch and Bound

  • Complete?

    • Yes

  • Optimal?

    • Yes

      • As long as it runs to completion

      • As long as bound function is optimistic (a lower bound)

  • Time?

    • O(bm), Exponential

  • Space?

    • O(bm)

      • At worst, keeps all states in memory and does not prune.

      • At best?


Assignment
Assignment

  • HW #27: A* for the 8-puzzle


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