At top level, call ANGELIC -SEARCH with as the initialPlan. The predicate MAKING-PROGRESS checks to make sure that we aren’t stuck in an infinite regression of refinements. clauses return state def angelic_search ( problem, hierarchy, initialPlan ): """ A hierarchical planning algorithm that uses angelic semantics to identify and commit to high-level plans that work while avoiding high-level plans that don’t. parent, sequence )) def result ( state, actions ): """The outcome of applying an action to the current problem""" for a in actions : if a. Any solvable blocks-world instance, however, can be transformed into a serially decomposable problem. This is be- cause the dependencies from pre- to postconditions in operators have circularities. refinements ( hla, outcome, hierarchy ): print ( "." ) frontier. The blocks world problem is an example of a problem that does not have serially decomposable operators. path () else : print ( "else" ) for sequence in Problem. result ( problem, prefix ) if hla is None : if outcome. action # prefix, suffix = subseq(plan.state, hla) outcome = Problem. state # first_or_null(plan) prefix = None if plan. append ( act ) while True : if not frontier : return None plan = frontier.
SUSSMAN ANOMALY IN BLOCK WORLD PROBLEM GENERATOR
append ( HLA ( library, precond, effect )) yield actions def hierarchical_search ( problem, hierarchy ): """ 'Hierarchical Search, a Breadth First Search implementation of Hierarchical Forward Planning Search' The problem is a real-world problem defined by the problem class, and the hierarchy is a dictionary of HLA - refinements (see refinements generator for details) """ act = Node ( problem. name ] for i in indices : actions = for j in range ( len ( library )): # find the index of the step of the HLA index_step = ) if x = library ] precond = library # preconditions of step effect = library # effect of step actions. name = action_name ) if list_action is None : raise Exception ( "Action ' """ e = Expr ( hla.
args list_action = first ( a for a in self. Note that action is an Expr like expr('Remove(Glass, Table)') or expr('Eat(Sandwich)') """ action_name = action. goals ) def act ( self, action ): """ Performs the action given as argument. append ( clause ) return new_clauses def goal_test ( self ): """Checks if the goals have been reached""" return all ( goal in self. actions = actions def convert ( self, clauses ): """Converts strings into exprs""" if not isinstance ( clauses, Expr ): if len ( clauses ) > 0 : clauses = expr ( clauses ) else : clauses = try : clauses = conjuncts ( clauses ) except AttributeError : clauses = clauses new_clauses = for clause in clauses : if clause. """ def _init_ ( self, init, goals, actions ): self. The Sussman Anomaly illustrates that a) even simple blocks problems may have non-serializable sub-goals b) even simple blocks problems can have serializable sub-goals c) Goal Stack Planning always finds the shortest plan in the blocks world domain d) Backward State Spec Planning 14. The conjunction of these logical statements completely defines a state. It stores states in a knowledge base consisting of first order logic statements. Class PlanningProblem : """ Planning Domain Definition Language (PlanningProblem) used to define a search problem.
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