problem solving and search in ai part ii
DESCRIPTION
Problem Solving and Search in AI Part II. Search Strategies. Uninformed (blind, exhaustive) strategies use only the information available in the problem definition Breadth-first search Depth-first search Uniform-cost search - PowerPoint PPT PresentationTRANSCRIPT
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Problem Solving and Problem Solving and Search in AI Search in AI
Part IIPart II
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Search StrategiesSearch StrategiesUninformedUninformed (blind, exhaustive) (blind, exhaustive) strategies use strategies use only the information available in the problem only the information available in the problem definitiondefinition Breadth-first searchBreadth-first search Depth-first searchDepth-first search Uniform-cost searchUniform-cost search
HeuristicHeuristic strategies use “rules of thumb” based strategies use “rules of thumb” based on the knowledge of domain to pick between on the knowledge of domain to pick between alternatives at each stepalternatives at each step
Graph Searching Applet:http://www.cs.ubc.ca/labs/lci/CIspace/Version4/search/index.html
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Basic Search ProcedureBasic Search Procedure
1. Start with the start node (root of the search 1. Start with the start node (root of the search tree) and place in on the tree) and place in on the queuequeue
2. Remove the front node in the queue and 2. Remove the front node in the queue and If the node is a If the node is a goal nodegoal node, then we are done; stop., then we are done; stop. Otherwise Otherwise expandexpand the node the node generate its children generate its children
using the successor function (other states that can be using the successor function (other states that can be reached with one move)reached with one move)
3. Place the children on the queue according to 3. Place the children on the queue according to the the search strategysearch strategy
4. Go back to step 2.4. Go back to step 2.
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Search StrategiesSearch Strategies
Search strategies differ based on the order Search strategies differ based on the order in which new successor nodes are added in which new successor nodes are added to the queueto the queueBreadth-first Breadth-first add nodes to the end of add nodes to the end of the queuethe queueDepth-first Depth-first add nodes to the front add nodes to the frontUniform cost Uniform cost sort the nodes on the sort the nodes on the queue based on the cost of reaching the queue based on the cost of reaching the node from start nodenode from start node
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Breadth-First SearchBreadth-First Search
add nodes to the end of the queueadd nodes to the end of the queue
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
A
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
B I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
I E C D
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
E C D M K
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
C D M K F H
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
D M K F H N
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
M K F H N
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
K F H N O G
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
F H N O G
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
H N O G Q
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
N O G Q R T
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
O G Q R T P S
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
G Q R T P S
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
Q R T P S
DONE!!!
Solution: A I M G
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Depth-First SearchDepth-First Search
add nodes to the front of the queueadd nodes to the front of the queue
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
A
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
B I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
C D I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
N D I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
P S D I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
S D I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
D I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
I E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
M K E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
O G K E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
G K E
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Breadth-First SearchBreadth-First SearchA
IB E
C D M K
N
F H
O G Q R T
P S
goal
Queue:
K E
DONE!!!
Solution: A I M G
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3333
Search ExerciseSearch Exercise
Traffic Graph:
Start Node: A
Goal Node: M
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3434
Exercise: Breadth-FirstExercise: Breadth-FirstA
B D G
C E K L
B E F I D HO
I I J M
Order of node expansion:
A, B, D, G, C, E, K, L, B, E, F, I, O, D, H, I, I, J, M
Solution Found: A D E I M (optimal)
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3535
Exercise: Breadth-FirstExercise: Breadth-First(with duplicate detection)(with duplicate detection)
A
B D G
C E K L
F HOI
J M
Order of node expansion:
A, B, D, G, C, E, K, L, F, I, O, H, J, M
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Exercise : Depth-FirstExercise : Depth-FirstA
B D G
C E
B E F
I
M
Order of node expansion: A, B, D, C, B, E, I, M
Solution Found: A D C E I M (not optimal)
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Example: The 8-PuzzleExample: The 8-Puzzle
States?States? integer location of tilesinteger location of tiles
Operators? Operators? move blank left, right, up, downmove blank left, right, up, down
Goal Test?Goal Test? = goal state (given)= goal state (given)
Path Cost?Path Cost? One per moveOne per move
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We will assume that we can do repeated state checking(i.e., we will not generate nodes that have been previously expanded)
5 4
6 1 8
7 3 2
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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5 4
6 1 8
7 3 2
5 4
6 1 8
7 3 2
5 4 8
6 1
7 3 2
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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5 4
6 1 8
7 3 2
5 4
6 1 8
7 3 2
5 4 8
6 1
7 3 2
5 4
6 1 8
7 3 2
5 1 4
6 8
7 3 2
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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5 4
6 1 8
7 3 2
5 4
6 1 8
7 3 2
5 4 8
6 1
7 3 2
5 4
6 1 8
7 3 2
5 1 4
6 8
7 3 2
6 5 4
1 8
7 3 2
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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5 4
6 1 8
7 3 2
5 4
6 1 8
7 3 2
5 4 8
6 1
7 3 2
5 4
6 1 8
7 3 2
5 1 4
6 8
7 3 2
6 5 4
1 8
7 3 2
6 5 4
1 8
7 3 2
6 5 4
7 1 8
3 2
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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5 4
6 1 8
7 3 2
5 4
6 1 8
7 3 2
5 4 8
6 1
7 3 2
5 4
6 1 8
7 3 2
5 1 4
6 8
7 3 2
6 5 4
1 8
7 3 2
5 1 4
6 8
7 3 2
5 1 4
6 8
7 3 2
5 1 4
6 3 8
7 2
6 5 4
1 8
7 3 2
6 5 4
7 1 8
3 2
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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5 4
6 1 8
7 3 2
5 4
6 1 8
7 3 2
5 4 8
6 1
7 3 2
5 4
6 1 8
7 3 2
5 1 4
6 8
7 3 2
6 5 4
1 8
7 3 2
5 1 4
6 8
7 3 2
5 1 4
6 8
7 3 2
5 1 4
6 3 8
7 2
6 5 4
1 8
7 3 2
6 5 4
7 1 8
3 2
1 4
5 6 8
7 3 2
5 1 4
7 6 8
3 2
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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5 4
6 1 8
7 3 2
5 4
6 1 8
7 3 2
5 4 8
6 1
7 3 2
5 4
6 1 8
7 3 2
5 1 4
6 8
7 3 2
5 4 8
6 1
7 3 2
5 4 8
6 1 2
7 3
6 5 4
1 8
7 3 2
5 1 4
6 8
7 3 2
5 1 4
6 8
7 3 2
5 1 4
6 3 8
7 2
5 8
6 4 1
7 3 2
5 4 8
6 3 1
7 2
5 4 8
6 1
7 3 2
5 4 8
6 1 2
7 3
6 5 4
1 8
7 3 2
6 5 4
7 1 8
3 2
1 4
5 6 8
7 3 2
5 1 4
7 6 8
3 2 . . .
Example: 8-Puzzle – Depth First SearchExample: 8-Puzzle – Depth First Search
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Uniform-Cost SearchUniform-Cost Search Each move has some cost The cost of the path to each node N is g(N) = sum of the move costs The goal is to generate a solution path of minimal cost The queue is sorted in increasing cost order
So, lower cost nodes go to the front
S 0
1A 5B
15CS G
A
B
C
5
1
15
10
5
5
G11
G10
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Uniform-Cost SearchUniform-Cost Search
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Uniform-Cost SearchUniform-Cost SearchAlways expand the least-cost unexpanded nodeAlways expand the least-cost unexpanded node Queue = insert in order of increasing path costQueue = insert in order of increasing path cost
Arad
Sibiu TimisoaraZerind
75 140 118
<== Zerind, Timisoara, Sibiu <==<== Zerind, Timisoara, Sibiu <==
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Uniform-Cost SearchUniform-Cost Search
Arad
Sibiu TimisoaraZerind
75 140 118
OradeaArad
75+75 71+75
<== Timisoara, Sibiu, Oradea, Arad <==<== Timisoara, Sibiu, Oradea, Arad <==
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Uniform-Cost SearchUniform-Cost Search
Arad
Sibiu TimisoaraZerind
75 140 118
OradeaArad
75+75 71+75
LugoiArad
118+118 111+118
<== Sibiu, Oradea, Arad, Lugoi, Arad <==<== Sibiu, Oradea, Arad, Lugoi, Arad <==
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Uniform-Cost SearchUniform-Cost Search
Arad
Sibiu TimisoaraZerind
75 140 118
OradeaArad
75+75 71+75
LugoiArad
118+118 111+118
<== Sibiu, Oradea, Arad, Lugoi, Arad <==<== Sibiu, Oradea, Arad, Lugoi, Arad <==
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Uniform Cost SearchUniform Cost Search
For the rest of the example, let us assume For the rest of the example, let us assume repeated state checkingrepeated state checkingHowever for uniform cost search we always However for uniform cost search we always keep the state that has the least path cost from keep the state that has the least path cost from the start node.the start node.
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Uniform-Cost SearchUniform-Cost Search
Arad
Sibiu TimisoaraZerind
75 140 118
Oradea
71+75
Lugoi
111+118
<== Sibiu, Oradea, Lugoi <==<== Sibiu, Oradea, Lugoi <==
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Uniform-Cost SearchUniform-Cost Search
Arad
Sibiu TimisoaraZerind
75 140 118
Oradea
146
Lugoi
229
<== Oradea, Rimnicu, Lugoi, Fagaras <==<== Oradea, Rimnicu, Lugoi, Fagaras <==
Fagaras Rimnicu
239 220
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Uniform-Cost SearchUniform-Cost Search
Arad
Sibiu TimisoaraZerind
75 140 118
Oradea
146
Lugoi
229
<== Rimnicu, Lugoi, Fagaras <==<== Rimnicu, Lugoi, Fagaras <==
Fagaras Rimnicu
239 220
Note: Oradea only leads to repeated states.
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Uniform-Cost SearchUniform-Cost Search
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Uniform-Cost SearchUniform-Cost SearchArad
Sibiu TimisoaraZerind
75 140 118
Oradea
146
Lugoi
229
<== Lugoi, Fagaras, Pitesti, Craiova <==<== Lugoi, Fagaras, Pitesti, Craiova <==
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
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Uniform-Cost SearchUniform-Cost SearchArad
Sibiu TimisoaraZerind
75 140 118
Oradea
146
Lugoi
229
<== Fagaras, Mehadia, Pitesti, Craiova <==<== Fagaras, Mehadia, Pitesti, Craiova <==
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia
299
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Uniform-Cost SearchUniform-Cost SearchArad
Sibiu TimisoaraZerind
75 140 118
Oradea
146
Lugoi
229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia
299
Bucharest
450
<== Mehadia, Pitesti, Craiova, Bucharest <==<== Mehadia, Pitesti, Craiova, Bucharest <==
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LugoiOradea
Sibiu
Arad
TimisoaraZerind
Uniform-Cost SearchUniform-Cost Search
75 140 118
146 229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia299
Bucharest
450
<== Pitesti, Craiova, Dobreta, Bucharest <==<== Pitesti, Craiova, Dobreta, Bucharest <==
Dobreta374
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LugoiOradea
Sibiu
Arad
TimisoaraZerind
Uniform-Cost SearchUniform-Cost Search
75 140 118
146 229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia299
Bucharest
450
<== Craiova, Dobreta, Bucharest <==<== Craiova, Dobreta, Bucharest <==
Dobreta374Bucharest Craiova
418 455
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LugoiOradea
Sibiu
Arad
TimisoaraZerind
Uniform-Cost SearchUniform-Cost Search
75 140 118
146 229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia299
Bucharest
450
<== Craiova, Dobreta, Bucharest <==<== Craiova, Dobreta, Bucharest <==
Dobreta374Bucharest Craiova
418 455
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LugoiOradea
Sibiu
Arad
TimisoaraZerind
Uniform-Cost SearchUniform-Cost Search
75 140 118
146 229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia299
<== Craiova, Dobreta, Bucharest <==<== Craiova, Dobreta, Bucharest <==
Dobreta374
Bucharest
418
Goes to repeated states withhigher path costs than previousvisits to those states
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Uniform-Cost SearchUniform-Cost Search
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LugoiOradea
Sibiu
Arad
TimisoaraZerind
Uniform-Cost SearchUniform-Cost Search
75 140 118
146 229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia299
<== Bucharest <==<== Bucharest <==
Dobreta374
Bucharest
418
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LugoiOradea
Sibiu
Arad
TimisoaraZerind
Uniform-Cost SearchUniform-Cost Search
75 140 118
146 229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia299
<== Urziceni, Giurgiu <==<== Urziceni, Giurgiu <==
Dobreta374
Bucharest
418
Pitesti
519
Fagaras629
Giurgiu
508
Urziceni
503
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LugoiOradea
Sibiu
Arad
TimisoaraZerind
Uniform-Cost SearchUniform-Cost Search
75 140 118
146 229
Fagaras Rimnicu
239 220
Craiova Pitesti
367 317
Mehadia299
Dobreta374
Bucharest
418
Solution Path: Arad Sibiu Rimnicu Pitesti Bucharest
Total cost: 418
Compare this to: Arad Sibiu Fagaras Bucharest with total cost of 450
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Heuristic SearchHeuristic Search
Problem with uniform cost searchProblem with uniform cost search We are only considering the cost so far, not the expected cost of We are only considering the cost so far, not the expected cost of
getting to the goal nodegetting to the goal node But, we don’t know before hand the cost of getting to the goal But, we don’t know before hand the cost of getting to the goal
from a previous statefrom a previous state
Solution:Solution: Need to estimate for each state the cost of getting from there to Need to estimate for each state the cost of getting from there to
a goal statea goal state Use “Use “heuristicheuristic” information to guess which nodes to expand next” information to guess which nodes to expand next the heuristic is in the form of an evaluation function based on the heuristic is in the form of an evaluation function based on
domain-specific information related to the problem.domain-specific information related to the problem. the the evaluation functionevaluation function gives us a way to evaluate a node gives us a way to evaluate a node
“locally” based on an estimate of the cost to get from the node to “locally” based on an estimate of the cost to get from the node to a goal node (the idea is to find the least cost path to a goal a goal node (the idea is to find the least cost path to a goal node).node).