yap-6.3/Logtalk/examples/searching/SCRIPT.txt

=================================================================
Logtalk - Object oriented extension to Prolog
Release 2.29.1

Copyright (c) 1998-2006 Paulo Moura.  All Rights Reserved.
=================================================================


% start by loading the necessary library and support example files (if not 
% already loaded):

| ?- logtalk_load(library(all_loader)).
...

| ?- logtalk_load(roots(loader)).
...


% now you are ready for loading the example:

| ?- logtalk_load(searching(loader)).
...


% 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),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)],
Initial = ((3,3),left,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: 26
ratio solution length / state transitions: 0.461538
minimum branching degree: 1
average branching degree: 2.30769
maximum branching degree: 3
time: 0.02

Cost = 15,
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)],
Initial = ((3,3),left,0,0) ? 

yes


% bridge problem, solved using a hill climbing strategy

| ?- performance::init, bridge::initial_state(Initial), hill_climbing(30)::solve(bridge, Initial, Path, Cost), bridge::print_path(Path), performance::report.

 _|____________|_ lamp 1 3 6 8 12 
1 3  lamp _|____________|_ 6 8 12 
3  _|____________|_ lamp 1 6 8 12 
1 3 6  lamp _|____________|_ 8 12 
3 6  _|____________|_ lamp 1 8 12 
3 6 8 12  lamp _|____________|_ 1 
6 8 12  _|____________|_ lamp 1 3 
1 3 6 8 12  lamp _|____________|_ 
solution length: 8
state transitions: 367
ratio solution length / state transitions: 0.0217984
minimum branching degree: 1
average branching degree: 7.32579
maximum branching degree: 15
time: 0.28

Initial = [], right, [1, 3, 6, 8, 12]
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, [...|...]), ([...|...], ..., ...)]
Cost = 29 

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: 109
ratio solution length / state transitions: 0.0550459
minimum branching degree: 2
average branching degree: 3.63158
maximum branching degree: 4
time: 0.02

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: 0.666667
minimum branching degree: 1
average branching degree: 2
maximum branching degree: 3
time: 0.00

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

yes


% salt puzzle using breadth first search

| ?- 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.

(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)
solution length: 6
state transitions: 476
ratio solution length / state transitions: 0.0126050420168067
minimum branching degree: 1
average branching degree: 4.05829596412556
maximum branching degree: 6
time: 0.0899999999999999

Initial = 0, 0, 0, all_empty
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))]

yes


| ?- 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.

(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)
solution length: 8
state transitions: 3037
ratio solution length / state transitions: 0.00263417846559104
minimum branching degree: 1
average branching degree: 4.22404371584699
maximum branching degree: 6
time: 1.41

Initial = 0, 0, 0, all_empty
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))]

yes


| ?- 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.

(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)
solution length: 12
state transitions: 289904
ratio solution length / state transitions: 4.13930128594293e-5
minimum branching degree: 1
average branching degree: 4.50438946528332
maximum branching degree: 6
time: 1882.81

Initial = 0, 0, 0, all_empty
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))]

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: 3.13333
maximum branching degree: 4
time: 0.01

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: 3.13333
maximum branching degree: 4
time: 0.02

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.