#Sussman anomaly in block world problem how to
#Sussman anomaly in block world problem series

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 conjunction of these logical statements completely defines a state. It stores states in a knowledge base consisting of first order logic statements. Here are the solutions.Class PlanningProblem : ''' Planning Domain Definition Language (PlanningProblem) used to define a search problem. Only one block or stack is permitted per table. Example Output from Blocks World Problem #3īlocks are stacked ab on table 1. Available actions include 'move', 'stack', and 'unstack'. The AI needs to plan for moving a block temporarily to table 2, while it sets up the correct order for stacking on table 3. Only one block or stack is permitted on a table. The fun one! Unstack blocks ba from table 1 to a stack ab on table 3. Multiple blocks are permitted on a table. Unstacks blocks ba from table x to a stack ab on table y. Moves blocks a, b from table x to a stack ab on table y. Move blocks a, b from table x to table y.

Apply actions on current state to produce child states.

Identify valid actions for the current state.

The resulting tree of possible states and actions may then be traversed using the A* algorithm to locate an optimal set of steps to achieve the goal state, as specified in the problem. This process is repeated, where applicable actions are identified for the new states, applied, and further new states produced.

The actions are then applied to the current state, producing a new set of states. The JSON is then processed to identify applicable actions within a given state of the problem. The domain and problem PDDL files are parsed via PEG.js, producing a JSON object for a given domain. The AI planning works by processing a simple domain using a PEG.js grammar sheet and runs the result using a simple STRIPS problem.

#Sussman anomaly in block world problem series

Several examples from the Blocks World domain are included in this project, in which the AI is able to successfully plan the series of steps to move and stack blocks on a series of tables.

For example, stacking blocks, Towers of Hanoi, and even Starcraft can be solved by the AI (see below, it's pretty neat!).

#Sussman anomaly in block world problem how to

The following examples show how to solve planning problems by identifying the optimal set of actions to achieve a goal. If you're new to STRIPS automated planning, here is a great tutorial to get you started.