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Merge pull request #79 from APLA-Toolbox/hsp-heuristic
[Heuristics] Update architecture / Implement HSP
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,3 @@ | ||
| from .heuristics import zero_heuristic | ||
| from .node import Node | ||
| import logging | ||
| import math | ||
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||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,10 +1,125 @@ | ||
| def zero_heuristic(state, automated_planner): | ||
| return 0 | ||
| import logging | ||
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| def goal_count_heuristic(state, automated_planner): | ||
| count = 0 | ||
| for goal in automated_planner.goals: | ||
| if not automated_planner.state_has_term(state, goal): | ||
| count += 1 | ||
| return count | ||
| class BasicHeuristic: | ||
| def __init__(self, automated_planner, heuristic_key): | ||
| self.automated_planner = automated_planner | ||
| self.heuristic_keys = { | ||
| "basic/zero": self.__zero_heuristic, | ||
| "basic/goal_count": self.__goal_count_heuristic, | ||
| } | ||
| if heuristic_key not in list(self.heuristic_keys.keys()): | ||
| logging.warning( | ||
| "Heuristic key isn't registered, forcing it to [basic/goal_count]" | ||
| ) | ||
| heuristic_key = "basic/goal_count" | ||
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| self.current_h = heuristic_key | ||
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| def compute(self, state): | ||
| return self.heuristic_keys[self.current_h](state) | ||
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| def __zero_heuristic(self, state): | ||
| return 0 | ||
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| def __goal_count_heuristic(self, state): | ||
| count = 0 | ||
| for goal in self.automated_planner.goals: | ||
| if not self.automated_planner.state_has_term(state, goal): | ||
| count += 1 | ||
| return count | ||
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| class DeleteRelaxationHeuristic: | ||
| def __init__(self, automated_planner, heuristic_key): | ||
| class DRHCache: | ||
| def __init__(self, domain=None, axioms=None, preconds=None, additions=None): | ||
| self.domain = domain | ||
| self.axioms = axioms | ||
| self.preconds = preconds | ||
| self.additions = additions | ||
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||
| self.automated_planner = automated_planner | ||
| self.cache = DRHCache() | ||
| self.heuristic_keys = { | ||
| "delete_relaxation/h_add": self.__h_add, | ||
| "delete_relaxation/h_max": self.__h_max, | ||
| } | ||
| if heuristic_key not in list(self.heuristic_keys.keys()): | ||
| logging.warning( | ||
| "Heuristic key isn't registered, forcing it to [delete_relaxation/h_add]" | ||
| ) | ||
| heuristic_key = "delete_relaxation/h_add" | ||
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| self.current_h = heuristic_key | ||
| self.has_been_precomputed = False | ||
| self.__pre_compute() | ||
| # return self.heuristic_keys[self.current_h](state) | ||
|
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||
| def compute(self, state): | ||
| if not self.has_been_precomputed: | ||
| self.__pre_compute() | ||
| domain = self.cache.domain | ||
| goals = self.automated_planner.goals | ||
| types = state.types | ||
| facts = state.facts | ||
| fact_costs = self.automated_planner.pddl.init_facts_costs(facts) | ||
| while not ( | ||
| len(fact_costs) == self.automated_planner.pddl.length(facts) | ||
| and self.__facts_eq(fact_costs, facts) | ||
| ): | ||
| facts, state = self.automated_planner.pddl.get_facts_and_state( | ||
| fact_costs, types | ||
| ) | ||
| if self.automated_planner.satisfies(goals, state): | ||
| costs = [] | ||
| fact_costs_str = dict([(str(k), val) for k, val in fact_costs.items()]) | ||
| for g in goals: | ||
| if str(g) in fact_costs_str: | ||
| costs.append(fact_costs_str[str(g)]) | ||
| costs.insert(0, 0) | ||
| return self.heuristic_keys[self.current_h](costs) | ||
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| for ax in self.cache.axioms: | ||
| fact_costs = self.automated_planner.pddl.compute_costs_one_step_derivation( | ||
| facts, fact_costs, ax, self.current_h | ||
| ) | ||
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| actions = self.automated_planner.available_actions(state) | ||
| if not actions: | ||
| break | ||
| for act in actions: | ||
| fact_costs = self.automated_planner.pddl.compute_cost_action_effect( | ||
| fact_costs, act, domain, self.cache.additions, self.current_h | ||
| ) | ||
| return float("inf") | ||
|
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| def __pre_compute(self): | ||
| if self.has_been_precomputed: | ||
| return | ||
| domain = self.automated_planner.domain | ||
| domain, axioms = self.automated_planner.pddl.compute_hsp_axioms(domain) | ||
| # preconditions = dict() | ||
| additions = dict() | ||
| self.automated_planner.pddl.cache_global_preconditions(domain) | ||
| for name, definition in domain.actions.items(): | ||
| additions[name] = self.automated_planner.pddl.effect_diff( | ||
| definition.effect | ||
| ).add | ||
| self.cache.additions = additions | ||
| self.cache.preconds = self.automated_planner.pddl.g_preconditions | ||
| self.cache.domain = domain | ||
| self.cache.axioms = axioms | ||
| self.has_been_precomputed = True | ||
|
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| def __h_add(self, costs): | ||
| return sum(costs) | ||
|
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| def __h_max(self, costs): | ||
| return max(costs) | ||
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| def __facts_eq(self, facts_dict, facts_set): | ||
| for f in facts_set: | ||
| if not (f in facts_dict.keys()): | ||
| return False | ||
| return True |
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