yap-6.3/Logtalk/examples/searching/SCRIPT

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Logtalk - Object oriented extension to Prolog
Release 2.8.4

Copyright (c) 1998-2001 Paulo Moura.  All Rights Reserved.
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% farmer, cabbage, goat and wolf problem

| ?- farmer::initial_state(Initial), depth_first(10)::solve(farmer, Initial, Path), farmer::print_path(Path).

cgwf.<__>..........____
c_w_..........<__>.f_g_
c_wf.<__>..........__g_
__w_..........<__>.fcg_
_gwf.<__>.........._c__
_g__..........<__>.fc_w
_g_f.<__>.........._c_w
____..........<__>.fcgw

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)],
Initial = (north,north,north,north) ? 

yes


% missionaires and cannibals problem, solved using a hill-climbing strategy

| ?- miss_cann::initial_state(Initial), hill_climbing(16)::solve(miss_cann, Initial, Path, Cost), miss_cann::print_path(Path).

MMMCCC.<__>..........
MMCC..........<__>.MC
MMMCC.<__>..........C
MMM..........<__>.CCC
MMMC.<__>..........CC
MC..........<__>.MMCC
MMCC.<__>..........MC
CC..........<__>.MMMC
CCC.<__>..........MMM
C..........<__>.MMMCC
CC.<__>..........MMMC
..........<__>.MMMCCC

Cost = 15,
Path = [((3,3),esq,0,0),((2,2),dir,1,1),((3,2),esq,0,1),((3,0),dir,0,3),((3,1),esq,0,2),((1,1),dir,2,2),((2,2),esq,1,1),((0,2),dir,3,1),((0,3),esq,3,0),((0,1),dir,3,2),((0,2),esq,3,1),((0,0),dir,3,3)],
Initial = ((3,3),esq,0,0)
yes


% same problem as above with the addition of a monitor to measure hill-climbing performance

| ?- performance::init, miss_cann::initial_state(Initial), hill_climbing(16)::solve(miss_cann, Initial, Path, Cost), miss_cann::print_path(Path), performance::report.

MMMCCC.<__>..........
MMCC..........<__>.MC
MMMCC.<__>..........C
MMM..........<__>.CCC
MMMC.<__>..........CC
MC..........<__>.MMCC
MMCC.<__>..........MC
CC..........<__>.MMMC
CCC.<__>..........MMM
C..........<__>.MMMCC
CC.<__>..........MMMC
..........<__>.MMMCCC
solution length: 12
number of state transitions: 27
ratio solution length / state transitions: 0.4444444444444444
minimum branching degree: 2
average branching degree: 2.5555555555555554
maximum branching degree: 3
time: 0.067999999999756255

Cost = 15,
Path = [((3,3),esq,0,0),((2,2),dir,1,1),((3,2),esq,0,1),((3,0),dir,0,3),((3,1),esq,0,2),((1,1),dir,2,2),((2,2),esq,1,1),((0,2),dir,3,1),((0,3),esq,3,0),((0,1),dir,3,2),((0,2),esq,3,1),((0,0),dir,3,3)],
Initial = ((3,3),esq,0,0) ? 

yes


% water jugs problem solved using a breadth and a depth first strategy, with performance monitors
% it's interesting to compare the results

| ?- performance::init, water_jug::initial_state(Initial), breadth_first(6)::solve(water_jug, Initial, Path), water_jug::print_path(Path), performance::report.

4-gallon jug: 0
3-gallon jug: 0

4-gallon jug: 0
3-gallon jug: 3

4-gallon jug: 3
3-gallon jug: 0

4-gallon jug: 3
3-gallon jug: 3

4-gallon jug: 4
3-gallon jug: 2

4-gallon jug: 0
3-gallon jug: 2

solution length: 6
number of state transitions: 105
ratio solution length / state transitions: 0.05714285714285714
minimum branching degree: 2
average branching degree: 3.6315789473684212
maximum branching degree: 4
time: 0.20000000000027285

Path = [(0,0),(0,3),(3,0),(3,3),(4,2),(0,2)],
Initial = (0,0) ? 

yes


| ?- performance::init, water_jug::initial_state(Initial), depth_first(10)::solve(water_jug, Initial, Path), water_jug::print_path(Path), performance::report.

4-gallon jug: 0
3-gallon jug: 0

4-gallon jug: 4
3-gallon jug: 0

4-gallon jug: 4
3-gallon jug: 3

4-gallon jug: 0
3-gallon jug: 3

4-gallon jug: 3
3-gallon jug: 0

4-gallon jug: 3
3-gallon jug: 3

4-gallon jug: 4
3-gallon jug: 2

4-gallon jug: 0
3-gallon jug: 2

solution length: 8
number of state transitions: 12
ratio solution length / state transitions: 0.6666666666666666



minimum branching degree: 1
average branching degree: 2.0
maximum branching degree: 3
time: 0.021999999999934516

Path = [(0,0),(4,0),(4,3),(0,3),(3,0),(3,3),(4,2),(0,2)],
Initial = (0,0) ? 

yes


% eight puzzle solved using a hill-climbing strategy

| ?- 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.

283
164
7 5

283
1 4
765

2 3
184
765

 23
184
765

123
 84
765

123
8 4
765
solution length: 6
number of state transitions: 15
ratio solution length / state transitions: 0.4
minimum branching degree: 2
average branching degree: 3.1333333333333333
maximum branching degree: 4
time: 0.050000000000181899

Cost = 5,
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]],
Initial = [2/1,1/2,1/3,3/3,3/2,3/1,2/2,1/1,2/3] ? 

yes


% eight puzzle solved using a best-first strategy

| ?- 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.

283
164
7 5

283
1 4
765

2 3
184
765

 23
184
765

123
 84
765

123
8 4
765
solution length: 6
number of state transitions: 15
ratio solution length / state transitions: 0.4
minimum branching degree: 2
average branching degree: 3.1333333333333333
maximum branching degree: 4
time: 0.046000000000276486

Cost = 5,
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]],
Initial = [2/1,1/2,1/3,3/3,3/2,3/1,2/2,1/1,2/3] ? 

yes


% turn off performance monitor

| ?- performance::stop.