d79dd807e6
git-svn-id: https://yap.svn.sf.net/svnroot/yap/trunk@1744 b08c6af1-5177-4d33-ba66-4b1c6b8b522a
365 lines
9.7 KiB
Plaintext
365 lines
9.7 KiB
Plaintext
=================================================================
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Logtalk - Object oriented extension to Prolog
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Release 2.29.1
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Copyright (c) 1998-2006 Paulo Moura. All Rights Reserved.
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=================================================================
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% start by loading the necessary library and support example files (if not
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% already loaded):
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| ?- logtalk_load(library(all_loader)).
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...
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| ?- logtalk_load(roots(loader)).
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...
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% now you are ready for loading the example:
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| ?- logtalk_load(searching(loader)).
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...
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% farmer, cabbage, goat and wolf problem
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| ?- farmer::initial_state(Initial), depth_first(10)::solve(farmer, Initial, Path), farmer::print_path(Path).
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cgwf.<__>..........____
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c_w_..........<__>.f_g_
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c_wf.<__>..........__g_
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__w_..........<__>.fcg_
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_gwf.<__>.........._c__
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_g__..........<__>.fc_w
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_g_f.<__>.........._c_w
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____..........<__>.fcgw
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Path = [(north,north,north,north),(north,south,north,south),(north,south,north,north),(south,south,north,south),(south,north,north,north),(south,north,south,south),(south,north,south,north),(south,south,south,south)],
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Initial = (north,north,north,north) ?
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yes
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% missionaires and cannibals problem, solved using a hill-climbing strategy
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| ?- miss_cann::initial_state(Initial), hill_climbing(16)::solve(miss_cann, Initial, Path, Cost), miss_cann::print_path(Path).
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MMMCCC.<__>..........
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MMCC..........<__>.MC
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MMMCC.<__>..........C
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MMM..........<__>.CCC
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MMMC.<__>..........CC
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MC..........<__>.MMCC
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MMCC.<__>..........MC
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CC..........<__>.MMMC
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CCC.<__>..........MMM
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C..........<__>.MMMCC
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CC.<__>..........MMMC
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..........<__>.MMMCCC
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Cost = 15,
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Path = [((3,3),left,0,0),((2,2),right,1,1),((3,2),left,0,1),((3,0),right,0,3),((3,1),left,0,2),((1,1),right,2,2),((2,2),left,1,1),((0,2),right,3,1),((0,3),left,3,0),((0,1),right,3,2),((0,2),left,3,1),((0,0),right,3,3)],
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Initial = ((3,3),left,0,0)
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yes
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% same problem as above with the addition of a monitor to measure hill-climbing performance
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| ?- performance::init, miss_cann::initial_state(Initial), hill_climbing(16)::solve(miss_cann, Initial, Path, Cost), miss_cann::print_path(Path), performance::report.
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MMMCCC.<__>..........
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MMCC..........<__>.MC
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MMMCC.<__>..........C
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MMM..........<__>.CCC
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MMMC.<__>..........CC
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MC..........<__>.MMCC
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MMCC.<__>..........MC
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CC..........<__>.MMMC
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CCC.<__>..........MMM
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C..........<__>.MMMCC
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CC.<__>..........MMMC
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..........<__>.MMMCCC
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solution length: 12
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number of state transitions: 26
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ratio solution length / state transitions: 0.461538
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minimum branching degree: 1
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average branching degree: 2.30769
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maximum branching degree: 3
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time: 0.02
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Cost = 15,
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Path = [((3,3),left,0,0),((2,2),right,1,1),((3,2),left,0,1),((3,0),right,0,3),((3,1),left,0,2),((1,1),right,2,2),((2,2),left,1,1),((0,2),right,3,1),((0,3),left,3,0),((0,1),right,3,2),((0,2),left,3,1),((0,0),right,3,3)],
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Initial = ((3,3),left,0,0) ?
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yes
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% bridge problem, solved using a hill climbing strategy
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| ?- performance::init, bridge::initial_state(Initial), hill_climbing(30)::solve(bridge, Initial, Path, Cost), bridge::print_path(Path), performance::report.
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_|____________|_ lamp 1 3 6 8 12
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1 3 lamp _|____________|_ 6 8 12
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3 _|____________|_ lamp 1 6 8 12
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1 3 6 lamp _|____________|_ 8 12
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3 6 _|____________|_ lamp 1 8 12
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3 6 8 12 lamp _|____________|_ 1
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6 8 12 _|____________|_ lamp 1 3
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1 3 6 8 12 lamp _|____________|_
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solution length: 8
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state transitions: 367
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ratio solution length / state transitions: 0.0217984
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minimum branching degree: 1
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average branching degree: 7.32579
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maximum branching degree: 15
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time: 0.28
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Initial = [], right, [1, 3, 6, 8, 12]
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Path = [ ([], right, [1, 3, 6, 8, 12]), ([1, 3], left, [6, 8, 12]), ([3], right, [1, 6, 8, 12]), ([1, 3, 6], left, [8, 12]), ([3, 6], right, [1, 8|...]), ([3, 6|...], left, [1]), ([6|...], right, [...|...]), ([...|...], ..., ...)]
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Cost = 29
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yes
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% water jugs problem solved using a breadth and a depth first strategy, with performance monitors
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% it's interesting to compare the results
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| ?- performance::init, water_jug::initial_state(Initial), breadth_first(6)::solve(water_jug, Initial, Path), water_jug::print_path(Path), performance::report.
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4-gallon jug: 0
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3-gallon jug: 0
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4-gallon jug: 0
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3-gallon jug: 3
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4-gallon jug: 3
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3-gallon jug: 0
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4-gallon jug: 3
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3-gallon jug: 3
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4-gallon jug: 4
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3-gallon jug: 2
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4-gallon jug: 0
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3-gallon jug: 2
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solution length: 6
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number of state transitions: 109
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ratio solution length / state transitions: 0.0550459
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minimum branching degree: 2
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average branching degree: 3.63158
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maximum branching degree: 4
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time: 0.02
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Path = [(0,0),(0,3),(3,0),(3,3),(4,2),(0,2)],
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Initial = (0,0) ?
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yes
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| ?- performance::init, water_jug::initial_state(Initial), depth_first(10)::solve(water_jug, Initial, Path), water_jug::print_path(Path), performance::report.
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4-gallon jug: 0
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3-gallon jug: 0
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4-gallon jug: 4
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3-gallon jug: 0
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4-gallon jug: 4
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3-gallon jug: 3
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4-gallon jug: 0
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3-gallon jug: 3
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4-gallon jug: 3
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3-gallon jug: 0
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4-gallon jug: 3
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3-gallon jug: 3
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4-gallon jug: 4
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3-gallon jug: 2
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4-gallon jug: 0
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3-gallon jug: 2
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solution length: 8
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number of state transitions: 12
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ratio solution length / state transitions: 0.666667
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minimum branching degree: 1
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average branching degree: 2
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maximum branching degree: 3
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time: 0.00
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Path = [(0,0),(4,0),(4,3),(0,3),(3,0),(3,3),(4,2),(0,2)],
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Initial = (0,0) ?
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yes
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% salt puzzle using breadth first search
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| ?- performance::init, salt(100, 500, 200)::initial_state(Initial), breadth_first(6)::solve(salt(100, 500, 200), Initial, Path), salt(100, 500, 200)::print_path(Path), performance::report.
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(0, 0, 0) all_empty
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(0, 500, 0) fill(m1)
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(0, 300, 200) transfer(m1, m2)
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(0, 300, 0) empty(m2)
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(0, 100, 200) transfer(m1, m2)
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(100, 0, 200) transfer(m1, acc)
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solution length: 6
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state transitions: 476
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ratio solution length / state transitions: 0.0126050420168067
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minimum branching degree: 1
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average branching degree: 4.05829596412556
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maximum branching degree: 6
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time: 0.0899999999999999
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Initial = 0, 0, 0, all_empty
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Path = [(0, 0, 0, all_empty), (0, 500, 0, fill(m1)), (0, 300, 200, transfer(m1, m2)), (0, 300, 0, empty(m2)), (0, 100, 200, transfer(m1, m2)), (100, 0, 200, transfer(m1, acc))]
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yes
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| ?- performance::init, salt(200, 250, 550)::initial_state(Initial), breadth_first(7)::solve(salt(200, 250, 550), Initial, Path), salt(200, 250, 550)::print_path(Path), performance::report.
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(0, 0, 0) all_empty
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(0, 250, 0) fill(m1)
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(0, 0, 250) transfer(m1, m2)
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(0, 250, 250) fill(m1)
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(0, 0, 500) transfer(m1, m2)
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(0, 250, 500) fill(m1)
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(0, 200, 550) transfer(m1, m2)
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(200, 0, 550) transfer(m1, acc)
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solution length: 8
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state transitions: 3037
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ratio solution length / state transitions: 0.00263417846559104
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minimum branching degree: 1
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average branching degree: 4.22404371584699
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maximum branching degree: 6
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time: 1.41
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Initial = 0, 0, 0, all_empty
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Path = [(0, 0, 0, all_empty), (0, 250, 0, fill(m1)), (0, 0, 250, transfer(m1, m2)), (0, 250, 250, fill(m1)), (0, 0, 500, transfer(m1, m2)), (0, 250, 500, fill(m1)), (0, 200, 550, transfer(m1, m2)), (200, 0, 550, transfer(m1, acc))]
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yes
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| ?- performance::init, salt(100, 250, 550)::initial_state(Initial), breadth_first(11)::solve(salt(100, 250, 550), Initial, Path), salt(100, 250, 550)::print_path(Path), performance::report.
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(0, 0, 0) all_empty
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(0, 0, 550) fill(m2)
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(0, 250, 300) transfer(m2, m1)
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(0, 0, 300) empty(m1)
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(0, 250, 50) transfer(m2, m1)
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(50, 250, 0) transfer(m2, acc)
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(50, 0, 0) empty(m1)
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(50, 0, 550) fill(m2)
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(50, 250, 300) transfer(m2, m1)
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(50, 0, 300) empty(m1)
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(50, 250, 50) transfer(m2, m1)
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(100, 250, 0) transfer(m2, acc)
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solution length: 12
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state transitions: 289904
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ratio solution length / state transitions: 4.13930128594293e-5
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minimum branching degree: 1
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average branching degree: 4.50438946528332
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maximum branching degree: 6
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time: 1882.81
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Initial = 0, 0, 0, all_empty
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Path = [(0, 0, 0, all_empty), (0, 0, 550, fill(m2)), (0, 250, 300, transfer(m2, m1)), (0, 0, 300, empty(m1)), (0, 250, 50, transfer(m2, m1)), (50, 250, 0, transfer(m2, acc)), (50, 0, 0, empty(m1)), (50, 0, 550, fill(m2)), (50, 250, 300, transfer(m2, m1)), (50, 0, 300, empty(m1)), (50, 250, 50, transfer(m2, m1)), (100, 250, 0, transfer(m2, acc))]
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yes
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% eight puzzle solved using a hill-climbing strategy
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| ?- performance::init, eight_puzzle::initial_state(five_steps, Initial), hill_climbing(25)::solve(eight_puzzle, Initial, Path, Cost), eight_puzzle::print_path(Path), performance::report.
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283
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164
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7 5
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283
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1 4
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765
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2 3
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184
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765
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23
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184
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765
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123
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84
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765
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123
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8 4
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765
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solution length: 6
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number of state transitions: 15
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ratio solution length / state transitions: 0.4
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minimum branching degree: 2
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average branching degree: 3.13333
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maximum branching degree: 4
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time: 0.01
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Cost = 5,
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Path = [[2/1,1/2,1/3,3/3,3/2,3/1,2/2,1/1,2/3],[2/2,1/2,1/3,3/3,3/2,3/1,2/1,1/1,2/3],[2/3,1/2,1/3,3/3,3/2,3/1,2/1,1/1,2/2],[1/3,1/2,2/3,3/3,3/2,3/1,2/1,1/1,2/2],[1/2,1/3,2/3,3/3,3/2,3/1,2/1,1/1,2/2],[2/2,1/3,2/3,3/3,3/2,3/1,2/1,1/1,1/2]],
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Initial = [2/1,1/2,1/3,3/3,3/2,3/1,2/2,1/1,2/3] ?
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yes
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% eight puzzle solved using a best-first strategy
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| ?- performance::init, eight_puzzle::initial_state(five_steps, Initial), best_first(25)::solve(eight_puzzle, Initial, Path, Cost), eight_puzzle::print_path(Path), performance::report.
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283
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164
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7 5
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283
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1 4
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765
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2 3
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184
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765
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23
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184
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765
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123
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84
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765
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123
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8 4
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765
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solution length: 6
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number of state transitions: 15
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ratio solution length / state transitions: 0.4
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minimum branching degree: 2
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average branching degree: 3.13333
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maximum branching degree: 4
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time: 0.02
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Cost = 5,
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Path = [[2/1,1/2,1/3,3/3,3/2,3/1,2/2,1/1,2/3],[2/2,1/2,1/3,3/3,3/2,3/1,2/1,1/1,2/3],[2/3,1/2,1/3,3/3,3/2,3/1,2/1,1/1,2/2],[1/3,1/2,2/3,3/3,3/2,3/1,2/1,1/1,2/2],[1/2,1/3,2/3,3/3,3/2,3/1,2/1,1/1,2/2],[2/2,1/3,2/3,3/3,3/2,3/1,2/1,1/1,1/2]],
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Initial = [2/1,1/2,1/3,3/3,3/2,3/1,2/2,1/1,2/3] ?
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yes
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% turn off performance monitor
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| ?- performance::stop.
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