trí tuệ nhân tạo cao hoàng trứ chương ter3 heuristic search sinhvienzone com

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Heuristic Search

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Chapter 3

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Generate-and-test

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Best-first search

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Simulated annealing

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Hill climbing

Means-ends analysis

Constraint satisfaction

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Outline

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CSE Faculty - HCMUT
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Algorithm
1. Generate a possible solution.

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Generate-and-Test

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2. Test to see if this is actually a solution.

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3. Quit if a solution has been found.
Otherwise, return to step 1.

Cao Hoang Tru
CSE Faculty - HCMUT

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Generate-and-Test

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• Acceptable for simple problems.

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• Inefficient for problems with large space.

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CSE Faculty - HCMUT
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• Exhaustive generate-and-test.

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Generate-and-Test

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• Heuristic generate-and-test: not consider paths that

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• Plan generate-test:

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seem unlikely to lead to a solution.

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− Create a list of candidates.
− Apply generate-and-test to that list.

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CSE Faculty - HCMUT
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Generate-and-Test

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Example: coloured blocks
“Arrange four 6-sided cubes in a row, with each side of
each cube painted one of four colours, such that on all four
sides of the row one block face of each colour is showing.”

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CSE Faculty - HCMUT
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Generate-and-Test

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Example: coloured blocks

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Heuristic: if there are more red faces than other colours
then, when placing a block with several red faces, use few
of them as possible as outside faces.

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CSE Faculty - HCMUT
SinhVienZone.com

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Hill Climbing

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• Searching for a goal state = Climbing to the top of a hill

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CSE Faculty - HCMUT
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Hill Climbing

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• Generate-and-test + direction to move.

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is to a goal state.

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• Heuristic function to estimate how close a given state

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CSE Faculty - HCMUT
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Algorithm
1. Evaluate the initial state.

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Simple Hill Climbing

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2. Loop until a solution is found or there are no new
operators left to be applied:

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− Select and apply a new operator
− Evaluate the new state:
goal → quit
better than current state → new current state

Cao Hoang Tru
CSE Faculty - HCMUT
SinhVienZone.com

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Algorithm
1. Evaluate the initial state.

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Simple Hill Climbing

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2. Loop until a solution is found or there are no new
operators left to be applied:

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− Select and apply a new operator

− Evaluate the new state:
goal → quit
better than current state → new current state

Cao Hoang Tru
CSE Faculty - HCMUT
SinhVienZone.com

not try all possible new states!
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Simple Hill Climbing

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Example: coloured blocks

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Heuristic function: the sum of the number of different
colours on each of the four sides (solution = 16).

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CSE Faculty - HCMUT
SinhVienZone.com

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Simple Hill Climbing
• Heuristic function as a way to inject task-specific

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knowledge into the control process.

Cao Hoang Tru
CSE Faculty - HCMUT
SinhVienZone.com

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Steepest-Ascent Hill Climbing
(Gradient Search)

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• Considers all the moves from the current state.

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• Selects the best one as the next state.

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CSE Faculty - HCMUT
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Algorithm
1. Evaluate the initial state.

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Steepest-Ascent Hill Climbing
(Gradient Search)

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2. Loop until a solution is found or a complete iteration
produces no change to current state:

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− Apply all the possible operators
− Evaluate the best new state:
goal → quit
better than current state → new current state

Cao Hoang Tru
CSE Faculty - HCMUT
SinhVienZone.com

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Algorithm
1. Evaluate the initial state.

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Steepest-Ascent Hill Climbing
(Gradient Search)

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2. Loop until a solution is found or a complete iteration
produces no change to current state:

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− SUCC = a state such that any possible successor of the
current state will be better than SUCC (the worst state).
− For each operator that applies to the current state, evaluate
the new state:
goal → quit
better than SUCC → set SUCC to this state
− SUCC is better than the current state → set the new current
Cao Hoang Trustate to SUCC.
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Hill Climbing: Disadvantages

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Local maximum
A state that is better than all of its neighbours, but not
better than some other states far away.

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CSE Faculty - HCMUT
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Hill Climbing: Disadvantages

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Plateau
A flat area of the search space in which all neighbouring
states have the same value.

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CSE Faculty - HCMUT
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Hill Climbing: Disadvantages

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Ridge
The orientation of the high region, compared to the set
of available moves, makes it impossible to climb up.
However, two moves executed serially may increase
the height.

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CSE Faculty - HCMUT
SinhVienZone.com

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Hill Climbing: Disadvantages

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Ways Out

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different direction.

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• Backtrack to some earlier node and try going in a

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• Make a big jump to try to get in a new section.
• Moving in several directions at once.

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CSE Faculty - HCMUT
SinhVienZone.com

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• Hill climbing is a local method:

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Hill Climbing: Disadvantages

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Decides what to do next by looking only at the

“immediate” consequences of its choices.

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functions.

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• Global information might be encoded in heuristic

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CSE Faculty - HCMUT
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Goal

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Start

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Hill Climbing: Blocks World

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CSE Faculty - HCMUT
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Start

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Hill Climbing: Blocks World
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Local heuristic:
+1 for each block that is resting on the thing it is supposed to
be resting on.
−1 for each block that is resting on a wrong thing.
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CSE Faculty - HCMUT
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CSE Faculty - HCMUT
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CSE Faculty - HCMUT
SinhVienZone.com

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Hill Climbing: Blocks World

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