最后，代码是正确的，但组合爆炸杀死了它。
数据：
- 在 9'755 调用 5 之后，3 位，3 块
plan/3成功。
- 在 98'304 调用 5 之后，4 位，3 块
plan/3成功。
- 在 915'703 调用 后，3 位，4 块
plan/3成功，7 步。
- 在 97'288'255 呼叫 后，3 位，5 块
plan/3成功，9 步。
尝试更多是没有意义的，尤其是4 个地方，7 个街区。很明显，需要更进一步的启发式、利用对称性等。所有这些都需要更多的内存。在这里，在所有情况下使用的内存都很小：迭代加深（并存储在堆栈上）搜索树中只有一条路径在任何时候都是有效的。我们不记得访问过的任何状态或任何内容，这是一个非常简单的搜索。
在更新的代码下方（LONG，337 行）
更改（代码中标有“FIX”的重要更改）
library(list)在可能的情况下使用了谓词，去掉了一些代码。format/2使用添加调试输出生成。- 断言（见这里）使用
assertion/1添加来检查发生的事情是我认为发生的事情。
- 谓词和变量重命名以更好地反映其预期含义。
run/0添加的谓词初始化状态和目标，调用plan/3和美化计划。can/2混淆地结合了两个独立的方面：实例化一个动作和确定它的前提条件。分为两个谓词instantiate_action/1和preconditions/2。select_goal/2看起来它取决于状态，但实际上并没有。清理干净了。
请注意使它成为“迭代深化”搜索的技巧。它非常聪明，但仔细想想，它太聪明了一半，因为它基于谓词run/3，当使用 Plan 作为未绑定变量调用时，谓词的行为与使用 Plan 作为绑定变量时不同。第一种情况只发生在隐含搜索树的最顶端节点。这可能会在我没有的教科书中进一步解释，并且花了一些时间才意识到这段代码中实际发生了什么。
((nonvar(Plan), Plan == []) -> fail ; true )如果我放在搜索分支开头的修剪表达式很plan/3烦人，那么迭代加深技巧也应该如此。恕我直言，最好使用树深度计数器并通过累加器返回计划。特别是如果有人将负责在生产系统（即“生产系统”，而不是“基于前向链接规则的系统”）中维护此类代码。
% Based on
%
% Exercise 17.5 on page 429 of "Prolog Programming for Artificial Intelligence"
% by Ivan Bratko, 3rd edition
%
% The text says:
%
% "This planner searches through the state space in iterative-deepening style."
%
% See also:
%
% https://en.wikipedia.org/wiki/Iterative_deepening_depth-first_search
% https://en.wikipedia.org/wiki/Blocks_world
%
% The "iterative deepening" trick is all in the "Plan" list structure.
% If you remove it, the search becomes depth-first and no longer terminates!
% ----------
% Encapsulator to be called by user from the toplevel
% ----------
run :-
% Setting up
start_state(State),
final_state(Goals),
% TODO: Build predicates that verify that State and Goal are actually validly constructed
% Or choose better representations
nb_setval(glob_plancalls,0), % global variable for counting calls (non-backtrackable)
b_setval(glob_depth,0), % global variable for counting depth (backtrable)
% plan/3 is backtrackable and generates different/successively longer plans on backtrack
% it may however generate the same plan several times
plan(State, Goals, Plan),
dump_plan(Plan,1).
% ----------
% Writing out a solution found
% ----------
dump_plan([P|R],N) :-
% TODO: Verify that the plan indeed works!
format('Plan step ~w: ~w~n',[N,P]),
NN is N+1,
dump_plan(R,NN).
dump_plan([],_).
% The representation of the blocks world (see below) is a bit unfortunate as places and blocks
% have to be declared separately and relationships between places and blocks, as well
% as among blocks themselves have to declared explicitely and consistently.
% Additionally we have to specify which elements have a view of the sky (i.e. are clear/1)
% On the other hand, the final state and end state need not be specified fully, which is
% interesting (not sure what that means exactly regarding solution finding)
% The atoms used in describing places and blocks must be distinct due to program construction!
start_state([on(a,1), on(b,a), on(c,b), clear(c), clear(2), clear(3), clear(4)]).
final_state([on(a,2), on(b,a), on(c,b), clear(c), clear(1), clear(3), clear(4)]).
% ----------
% Representation of the blocks world
% ----------
% We have BLOCKs identified by atoms a,b,c, ...
% Each of those is identified by block/1 attribute.
% A block/1 is clear/1 if there is nothing on top of it.
% A block/1 is on(Block, Object) where Object is a block/1 or place/1.
block(a).
block(b).
block(c).
% We have PLACEs (i.e. columns of blocks) onto which to stack blocks.
% Each of these is identified by place/1 attribute.
% A place/1 can be clear/1 if there is nothing on top of it.
% (In fact these are like special immutable blocks and should be modeled as such)
place(1).
place(2).
place(3).
place(4).
% OBJECTs are place/1 or block/1.
object(X) :- place(X) ; block(X).
% ACTIONs are terms "move( Block, From, To)".
% "Block" must be block/1.
% "From" must be object/1 (i.e. block/1 or place/1).
% "To" must be object/1 (i.e. block/1 or place/1).
% Evidently constraints exist for a move/3 to be possible from or to any given state.
% STATEs are sets (implemented by lists) of "goal" terms.
% A goal term is "on( X, Y)" or "clear(Y)" where Y is object/1 and X is block/1.
% ----------
% plan( +State, +Goals, -Plan)
% Build a "Plan" get from "State" to "Goals".
% "State" and "Goals" are sets (implemented as lists) of goal terms.
% "Plan" is a list of action terms.
% The implementation works "backwards" from the "Goals" goal term list towards the "State" goal term list.
% ----------
% ___ Satisfaction branch ____
% This can only succeed if we are at the "end" of a Plan (the Plan must match '[]') and State matches Goal.
plan( State, Goals, []) :-
% Debugging output
nb_getval(glob_plancalls,P),
b_getval(glob_depth,D),
NP is P+1,
ND is D+1,
nb_setval(glob_plancalls,NP),
b_setval(glob_depth,ND),
statistics(stack,STACK),
format('plan/3 call ~w at depth ~d (stack ~d)~n',[NP,ND,STACK]),
% If the Goals statisfy State, print and succeed, otherwise print and fail
( satisfied( State, Goals) ->
(sort(Goals,Goals_s),
sort(State,State_s),
format(' Goals: ~w~n', [Goals_s]),
format(' State: ~w~n', [State_s]),
format(' *** SATISFIED ***~n'))
;
format(' --- NOT SATISFIED ---~n'),
fail).
% ____ Search branch ____
%
% Magic which generates the breath-first iterative deepening search:
%
% In the top node of the call tree (the node directly underneath "run"), "Plan" is unbound.
%
% At point "XXX" "Plan" is set to a list of as-yet-unbound actions of a given length.
% At each backtrack that reaches up to "XXX", "Plan" is bound to list longer by 1.
%
% In any other node of the call tree than the top node, "Plan" is bound to a list of fixed length
% becoming shorter by 1 on each recursive call.
%
% The length of that list determines how deep the search through the state space *must* go because
% satisfaction can only be happen if the "Plan" list is equal to [] and State matches Goal.
%
% So:
% On first activation of the top, build plans of length 0 (only possible if Goals passes satisfied/2 directly)
% On second activation of the top, build plans of length 1 (and backtrack over all possibilities of length 1)
% ...
% On Nth activation of the top, build plans of length N-1 (and backtrack over all possibilities of length N-1)
%
% A slight improvement is to fail the search branch immediately if Plan is a nonvar and is equal to []
% because append( PrePlan, [Action], Plan) will fail...
plan( State, Goals, Plan) :-
% The line below can be commented out w/o ill effects, it is just there to fail early
((nonvar(Plan), Plan == []) -> fail ; true ),
% Debugging output
nb_getval(glob_plancalls,P),
b_getval(glob_depth,D),
NP is P+1,
ND is D+1,
nb_setval(glob_plancalls,NP),
b_setval(glob_depth,ND),
statistics(stack,STACK),
format('plan/3 call ~w at depth ~d (stack ~d)~n',[NP,ND,STACK]),
format(' goals ~w~n',[Goals]),
% Even more debugging output
( var(Plan) -> format(' Top node of plan/3 call~n') ; true ),
( nonvar(Plan) -> (length(Plan,LP), format(' Low node of plan/3 call, plan length to complete: ~w~n',[LP])) ; true ),
% prevent runaway behaviour
% assertion(NP < 1000000),
% XXX
% append/3 is backtrackable.
% For the top node, it will generate longer completely uninstantiated PrePlans on backtracking:
% PrePlan = [], Plan = [Action] ;
% PrePlan = [_G981], Plan = [_G981, Action] ;
% PrePlan = [_G981, _G987], Plan = [_G981, _G987, Action] ;
% PrePlan = [_G981, _G987, _G993], Plan = [_G981, _G987, _G993, Action] ;
% For lower nodes, Plan is instantiated to a list of length N already, and PrePlan will therefore necessarily
% be the prefix list of length N-1
% XXX
append( PrePlan, [Action], Plan),
% Backtrackably select some concrete Goal from Goals
select_goal( Goals, Goal), % FIX: In the original this seems to depend on State, but it really doesn't
assert_goal(Goal),
format( ' Depth ~d, selected Goal: ~w~n',[ND,Goal]),
% Check whether Action achieves the Goal.
% As Action is free, what we actually do is instantiate Action backtrackably with something that achieves Goal
achieves( Action, Goal),
format( ' Depth ~d, selected Action: ~w~n', [ND,Action]),
% Fully instantiate Action backtrackably
% FIX: Passed "conditions", the precondition for a move, which is unused at this point: broken up into two calls
instantiate_action( Action),
format( ' Depth ~d, action instantiated to: ~w~n', [ND,Action]),
assertion(ground(Action)),
% Check that the Action does not clobber any of the Goals
preserves( Action, Goals),
% We now have a ground Action that "achieves" some goals in Goals while "preserving" all of them
% Work backwards from Goals to a "prior goals". regress/3 may fail to build a consistent GoalsPrior!
regress( Goals, Action, GoalsPrior),
plan( State, GoalsPrior, PrePlan).
% ----------
% Check
% ----------
assert_goal(X) :-
assertion(ground(X)),
assertion((X = on(A,B), block(A), object(B) ; X = clear(C), object(C))).
% ----------
% A State (a list) is satisfied by Goals (a list) if all the terms in Goals can also be found in State
% ----------
satisfied( State, Goals) :-
subtract( Goals, State, []). % Set difference yields empty list: [] = Goals - State
% ----------
% Backtrackably select a single Goal term from a set of Goals
% ----------
select_goal( Goals, Goal) :-
member( Goal, Goals).
% ----------
% When does an Action (move/2) achieve a Goal (clear/1, on/2)?
% This is called with instantiated Goal and free Action, so this actually instantiates Action
% with something (partially specified) that achieves Goal.
% ----------
achieves( Action, Goal) :-
assertion(var(Action)),
assertion(ground(Goal)),
would_add( Action, GoalsAdded),
member( Goal, GoalsAdded).
% ----------
% Given a ground Action and ground Goals, will Action from a State leading to Goals preserve Goals?
% ----------
preserves( Action, Goals) :-
assertion(ground(Action)),
assertion(ground(Goals)),
would_del( Action, GoalsDeleted),
intersection( Goals, GoalsDeleted, []). % "would delete none of the Goals"
% ----------
% Given existing Goals and an (instantiated) Action, compute the previous Goals
% that, when Action is applied, yield Goals. This may actually fail if no
% consistent GoalsPrior can be built!
% ** It is actually not at all self-evident that this is right and that we get a valid
% "GoalsPrior" via this method! ** (prove it!)
% FIX: "Condition" replaced by "Preconditions" which is what this is about.
% ----------
regress( Goals, Action, GoalsPrior) :-
assertion(ground(Action)),
assertion(ground(Goals)),
would_add( Action, GoalsAdded),
subtract( Goals, GoalsAdded, GoalsPriorPass), % from the "lists" library
preconditions( Action, Preconditions),
% All the Preconds must be fulfilled in Goals2, so try adding them
% Adding them may not succeed if inconsistencies appear in the resulting set of goals, in which case we fail
add_preconditions( Preconditions, GoalsPriorPass, GoalsPrior).
% ----------
% Adding preconditions to existing set of goals and checking for inconsistencies as we go
% Previously named addnew/3
% New we use union/3 from the "lists" library and the modified "consistent"
% ----------
add_preconditions( Preconditions, GoalsPriorIn, GoalsPriorOut) :-
add_preconditions_recur( Preconditions, GoalsPriorIn, GoalsPriorIn, GoalsPriorOut).
add_preconditions_recur( [], _, GoalsPrior, GoalsPrior).
add_preconditions_recur( [G|R], Goals, GoalsPriorAcc, GoalsPriorOut) :-
consistent( G, Goals),
union( [G], GoalsPriorAcc, GoalsPriorAccNext),
add_preconditions_recur( R, Goals, GoalsPriorAccNext, GoalsPriorOut).
% ----------
% Check whether a given Goal is consistent with the set of Goals to which it will be added
% Previously named "impossible/2".
% Now named "consistent/2" and we use negation as failure
% ----------
consistent( on(X,Y), Goals ) :-
\+ on(X,Y) = on(A,A), % this cannot ever happen, actually
\+ member( clear(Y), Goals ), % if X is on Y then Y cannot be clear
\+ ( member( on(X,Y1), Goals ), Y1 \== Y ), % Block cannot be in two places
\+ ( member( on(X1,Y), Goals), X1 \== X ). % Two blocks cannot be in same place
consistent( clear(X), Goals ) :-
\+ member( on(_,X), Goals). % if something is on X, X cannot be clear
% ----------
% Backtrackably instantiate a partially instantiated Action
% Previously named "can/2" and it also instantiated the "Condition", creating confusion
% ----------
instantiate_action(Action) :-
assertion(Action = move( Block, From, To)),
Action = move( Block, From, To),
block(Block), % will unify "Block" with a concrete block
object(To), % will unify "To" with a concrete object (block or place)
To \== Block, % equivalent to \+ == (but = would do here); this demands that blocks and places have disjoint sets of atoms
object(From), % will unify "From" with a concrete object (block or place)
From \== To,
Block \== From.
% ----------
% Find preconditions (a list of Goals) of a fully instantiated Action
% ----------
preconditions(Action, Preconditions) :-
assertion(ground(Action)),
Action = move( Block, From, To),
Preconditions = [clear(Block), clear(To), on(Block, From)].
% ----------
% would_del( Move, DelGoals )
% would_add( Move, AddGoals )
% If we run Move (assuming it is possible), what goals do we have to add/remove from an existing Goals
% ----------
would_del( move( Block, From, To), [on(Block,From), clear(To)] ).
would_add( move( Block, From, To), [on(Block,To), clear(From)] ).
运行上面的代码会产生大量的输出，最终：
plan/3 call 57063 at depth 6 (stack 98304)
Goals: [clear(2),clear(3),clear(4),clear(c),on(a,1),on(b,a),on(c,b)]
State: [clear(2),clear(3),clear(4),clear(c),on(a,1),on(b,a),on(c,b)]
*** SATISFIED ***
Plan step 1: move(c,b,3)
Plan step 2: move(b,a,4)
Plan step 3: move(a,1,2)
Plan step 4: move(b,4,a)
Plan step 5: move(c,3,b)
也可以看看