================================================================= Logtalk - Object oriented extension to Prolog Release 2.24.0 Copyright (c) 1998-2005 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 % 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.