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STRIPS is the Stanford Research Institute Planning System. I heard about the term STRIPS, but don't know what that means. Machine Learning, 8: 279–292.% Assignment 1% state(Coyote_location, Standing_on, Stonehenge_position, Holding_cannon, Shot_roadrunner)% Coyote_location: where is the coyote?% Standing_on: what is the coyote standing on (the stonehenge or the ground)?% Stonehenge_position: where is the stonehenge?% Holding_cannon: is the coyote holding the cannon?% Shot_roadrunner: has the coyote shot the roadrunner?% the coyote climbing off the stonehengetransform( state(Location, stonehenge, Location, X, Y), climb_down, state(Location, ground, Location, X, Y)).% the coyote shooting the roadrunner with the cannontransform( state(X, ground, Z, yes, _), shoot, state(X, ground, Z, yes, yes)).% the coyote getting the cannontransform( state(tree, stonehenge, tree, no, X), grab, state(tree, stonehenge, tree, yes, X)).% the coyote climbing onto the stonehengetransform( state(Location, ground, Location, X, Y), climb_up, state(Location, stonehenge, Location, X, Y)).% the coyote moving the boxtransform(state(Loc1, ground, Loc1, X, Y), move, state(Loc2, ground, Loc2, X, Y)).% the coyote running to a new positiontransform(state(Loc1, ground, Loc3, X, Y), run, state(Loc2, ground, Loc3, X, Y)).% achieve(StartState, Endstate)% True if EndState can be achieved from StartState% also prints Steps (in reverse order)achieve( InitialState, GoalState ) :- achieve( InitialState, GoalState, ).achieve( State, State, _ ).achieve( InitialState, GoalState, StateList ):- transform( InitialState, Step, NewState ), \+member( NewState, StateList ), achieve( NewState, GoalState, ), display( Step ), nl.query1 :- achieve(state(mountain, ground, river, no, no), state( _, _, _, _, yes)).% a predicate to display a list in reverse order (as it's faster and easier to prepend than append to the accumulated list of moves.)display_list().display_list( ) :- display_list(T), nl, display(H).% achieveb/2, a predicate to display moves in the correct order.achieveb( InitialState, GoalState ) :- achieveb( InitialState, GoalState,, ).% achieveb/3 a predicate used by achieveb/2 to display a list in the correct order.achieveb( State, State, DisplayList, _ ) :- display_list( DisplayList ).achieveb( InitialState, GoalState, DisplayList, StateList ):- transform( InitialState, Step, NewState), \+member( NewState, StateList ), achieveb( NewState, GoalState,, ).% query2, the same as query1, but using achieveb/2 instead of achieve/2query2 :- achieveb(state(mountain, ground, river, no, no), state( _, _, _, _, yes)).You can test it by either constructing your own queries, or using the predefined ones (query1. (1995) Temporal difference learning and TD-GAMMON. Journal of Logic Programming 19/20: 629–679. (1994) Inductive logic programming: Theory and methods. (1994) Inductive Logic Programming: Techniques and Applications. Journal of Artificial Intelligence Research, 4: 237–285. Kaelbling, L., Littman, M., and Moore, A. (1971) STRIPS: A new approach to the application of theorem proving. Workshop on Inductive Logic Programming, pages 133–141, Springer, Berlin.įikes, R.E., and Nilsson, N.J. (1997) Using logical decision trees for clustering. on Artificial Intelligence, Morgan Kaufmann, San Mateo, CA.ĭe Raedt, L., and Blockeel, H. (1991) Input generalization in delayed reinforcement learning: An algorithm and performance comparisons. Workshop on Inductive Logic Programming, pages 77–84, Springer, Berlin.Ĭhapman, D., and Kaelbling, L. (1997) Lookahead and discretization in ILP. Wadsworth, Belmont.īlockeel, H., and De Raedt, L. (1984) Classification and Regression Trees.
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(1997) Lazy incremental learning of control knowledge for efficiently obtaining quality plans. (1997) Experiments with Top-down Induction of Logical Decision Trees.