#include "stubborn_sets_simple.h"

#include "../plugins/plugin.h"
#include "../utils/logging.h"
#include "../utils/markup.h"

using namespace std;

namespace stubborn_sets_simple {
StubbornSetsSimple::StubbornSetsSimple(utils::Verbosity verbosity)
    : StubbornSetsActionCentric(verbosity) {
}

void StubbornSetsSimple::initialize(const shared_ptr<AbstractTask> &task) {
    StubbornSets::initialize(task);
    interference_relation.resize(num_operators);
    interference_relation_computed.resize(num_operators, false);
    log << "pruning method: stubborn sets simple" << endl;
}

const vector<int> &StubbornSetsSimple::get_interfering_operators(int op1_no) {
    /*
       TODO: as interference is symmetric, we only need to compute the
       relation for operators (o1, o2) with (o1 < o2) and add a lookup
       method that looks up (i, j) if i < j and (j, i) otherwise.
    */
    vector<int> &interfere_op1 = interference_relation[op1_no];
    if (!interference_relation_computed[op1_no]) {
        for (int op2_no = 0; op2_no < num_operators; ++op2_no) {
            if (op1_no != op2_no && interfere(op1_no, op2_no)) {
                interfere_op1.push_back(op2_no);
            }
        }
        interfere_op1.shrink_to_fit();
        interference_relation_computed[op1_no] = true;
    }
    return interfere_op1;
}

// Add all operators that achieve the fact (var, value) to stubborn set.
void StubbornSetsSimple::add_necessary_enabling_set(const FactPair &fact) {
    for (int op_no : achievers[fact.var][fact.value]) {
        enqueue_stubborn_operator(op_no);
    }
}

// Add all operators that interfere with op.
void StubbornSetsSimple::add_interfering(int op_no) {
    for (int interferer_no : get_interfering_operators(op_no)) {
        enqueue_stubborn_operator(interferer_no);
    }
}

void StubbornSetsSimple::initialize_stubborn_set(const State &state) {
    // Add a necessary enabling set for an unsatisfied goal.
    FactPair unsatisfied_goal = find_unsatisfied_goal(state);
    assert(unsatisfied_goal != FactPair::no_fact);
    add_necessary_enabling_set(unsatisfied_goal);
}

void StubbornSetsSimple::handle_stubborn_operator(const State &state,
                                                  int op_no) {
    FactPair unsatisfied_precondition = find_unsatisfied_precondition(op_no, state);
    if (unsatisfied_precondition == FactPair::no_fact) {
        /* no unsatisfied precondition found
           => operator is applicable
           => add all interfering operators */
        add_interfering(op_no);
    } else {
        /* unsatisfied precondition found
           => add a necessary enabling set for it */
        add_necessary_enabling_set(unsatisfied_precondition);
    }
}

class StubbornSetsSimpleFeature
    : public plugins::TypedFeature<PruningMethod, StubbornSetsSimple> {
public:
    StubbornSetsSimpleFeature() : TypedFeature("stubborn_sets_simple") {
        document_title("Stubborn sets simple");
        document_synopsis(
            "Stubborn sets represent a state pruning method which computes a subset "
            "of applicable operators in each state such that completeness and "
            "optimality of the overall search is preserved. As stubborn sets rely "
            "on several design choices, there are different variants thereof. "
            "This stubborn set variant resolves the design choices in a "
            "straight-forward way. For details, see the following papers: "
            + utils::format_conference_reference(
                {"Yusra Alkhazraji", "Martin Wehrle", "Robert Mattmueller", "Malte Helmert"},
                "A Stubborn Set Algorithm for Optimal Planning",
                "https://ai.dmi.unibas.ch/papers/alkhazraji-et-al-ecai2012.pdf",
                "Proceedings of the 20th European Conference on Artificial Intelligence "
                "(ECAI 2012)",
                "891-892",
                "IOS Press",
                "2012")
            + utils::format_conference_reference(
                {"Martin Wehrle", "Malte Helmert"},
                "Efficient Stubborn Sets: Generalized Algorithms and Selection Strategies",
                "http://www.aaai.org/ocs/index.php/ICAPS/ICAPS14/paper/view/7922/8042",
                "Proceedings of the 24th International Conference on Automated Planning "
                " and Scheduling (ICAPS 2014)",
                "323-331",
                "AAAI Press",
                "2014"));
        add_pruning_options_to_feature(*this);
    }

    virtual shared_ptr<StubbornSetsSimple>
    create_component(const plugins::Options &opts) const override {
        return plugins::make_shared_from_arg_tuples<StubbornSetsSimple>(
            get_pruning_arguments_from_options(opts));
    }
};

static plugins::FeaturePlugin<StubbornSetsSimpleFeature> _plugin;
}
