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tl;dr

Toy prolog planner to play with simple planning problems. I read "Artificial Intelligence: A Modern Approach", but there's no regression planning like STRIPS in here (yet). The ideas of having an intial state, a goal-check and a set of actions (with pre-conditions, Delete lists, Add lists) stay and are used in the forward dfs. The nice quirk: Preconditions can be callable prolog predicates, (subject to conditions). See "Blocks example"

This cannot be used for serious planning problems. If that's what you need, Use a real solver.

Components

  • A declarative problem representation based on action/4: action(ActionSignature, PreconditionList, DeleteList, AddList).
  • A DFS with a depth-limit (and sometimes loop-detection), to do forward planning.
  • A DFS as above, but with some partial-order-reduction based on sleep-sets.
  • A really inefficient way of supporting constraints on the state
    • by literally querying the state against the constraints after every action.
  • (in-progress) A minimal and (hopefully) easy way to explore plans which need some interaction, so you don't have to trace/spy.
  • A way to call python functions from prolog using an http server and JSON.
    • Should be easy to adapt for your own needs, but it may need some tuning.
    • See /json_client.pl and /python/*.py for more.
    • I should rework this to convert things to problog/pylo terms in the python space.

YOU'LL PROBABLY HAVE A BAD TIME IF YOU DON'T READ THIS:

On simple programs with no variables in the state

  • The initial state MUST be ground.
  • Any variable appearing in the Add/Delete List MUST appear in the ActionSignature
  • All variables in an evaluate must be ground post evaluation OR not appear in the Delete/AddList. These together should guarantee that any state will be ground by construction.

If/When I (or you?) sit down to finally write a backwards planner, I will hopefully allow variables to be in the state. I don't even know if that's possible, but for now I have another approach:

On programs with perform/variables

  • Performs MUST FAIL if they do not meet their promises.
  • If following actions make no assumptions about the value of the variables (this includes structure, which is complicated for lists), you should be fine.
  • All variables must be ground after a perform is performed.

Blocks Example

/examples/blocks_plan.pl contains a simple planning problem in the block world (from AI:AMA), You can see how the actions are declared.

/examples/blocks_evaluate2.pl shows how the same problem is declared with callable prolog.

Running the examples.

Running the whole search at once:

$ prolog testbed.pl
?- representation:initialize_problem('examples/blocks_plan.pl').
?- initial_state(InitialPredicates), state_create(InitialPredicates, _S),!, 
        search_forward_dfs(_S, goal_check, 4, GL).
    InitialPredicates = [on(a, b), on_table(b), on_table(c), clear(a), clear(c)],
    _S = assert_state_d(103, meta(56283180, 5)), % Depends on the state-rep in state_manipulation.pl
    GL = [[move_from_table(b, c), move_to_table(a, b)], [move_from_table(b, c), move_to_table(a, c), move_block_to_block(a, b, c)]]. % The goal list, yay!

Using the interative file: $ prolog testbed.pl ?- use_module(interactive). ?- representation:initialize_problem('examples/blocks_plan.pl'). ?- initial_state(SL), interactive_init(SL). true. % HIT ENTER!!!

?- query_current_state(P). % Wanna see the state?
    P = on(a, b) ;
    P = on_table(b) ;
    P = on_table(c) ;
    P = clear(a) ;
    P = clear(c).

?- perform_search([on(b,c)], 4, P).
    P = [move_from_table(b, c), move_to_table(a, b)] ;
    P = [move_from_table(b, c), move_to_table(a, c), move_block_to_block(a, b, c)].

?- apply_action_path([move_from_table(b, c), move_to_table(a, b)]).
    true . % HIT ENTER!!! I'll sort this out later.

?- query_current_state(P). % See, it changed :)
    P = on_table(c) ;
    P = clear(a) ;
    P = on_table(a) ;
    P = clear(b) ;
    P = on(b, c).