concepts.dm.pdsketch.crow.crow_planner_execution.crow_recursive_simple_with_execution#
- crow_recursive_simple_with_execution(executor, state, goal_expr, *, is_goal_serializable=True, is_goal_refinement_compressible=True, enable_reordering=False, enable_csp=False, enable_greedy_prefix_execution=True, max_search_depth=10, max_csp_branching_factor=5, max_beam_size=20, allow_empty_plan_for_optimistic_goal=False, verbose=True, verbose_rule_matching=False)[source]#
Compositional Regression and Optimization Wayfinder.
- Parameters:
executor (PDSketchExecutor) – the executor.
state (State) – the initial state.
goal_expr (str | ValueOutputExpression) – the goal expression.
is_goal_serializable (bool) – whether the goal is serialized already. Otherwise, it will be treated as a conjunction.
is_goal_refinement_compressible (bool) – whether the goals are refinement compressible.
enable_reordering (bool) – whether to enable reordering of subgoals in regression rules.
enable_csp (bool) – whether to enable CSP solving.
enable_greedy_prefix_execution (bool) – whether to enable the HPN execution strategy. As soon as we have found a sequence of actions (could be a prefix), we will execute them.
max_search_depth (int) – the maximum number of actions in a plan.
max_csp_branching_factor (int) – the maximum branching factor of the CSP solver.
max_beam_size (int) – the maximum beam size for keep tracking of CSP solutions.
allow_empty_plan_for_optimistic_goal (bool) – whether to allow empty plans for optimistic goals.
verbose (bool) – whether to print verbose information.
verbose_rule_matching (bool) – whether to print verbose information during the regression rule matching.
- Returns:
A list of plans. Each plan is a tuple of (actions, csp, initial_state, final_state).
- Return type: