A bit more cleanup to avoid duplicate testing and to separate the GOAP algorithm code from the little AI Manager thing.
parent
b2c1b220ac
commit
3f83d3f0bb
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#include "dbc.hpp" |
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#include "goap.hpp" |
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namespace ai { |
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using namespace nlohmann; |
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using namespace dbc; |
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bool is_subset(State& source, State& target) { |
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State result = source & target; |
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return result == target; |
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} |
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void Action::needs(int name, bool val) { |
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if(val) { |
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$positive_preconds[name] = true; |
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$negative_preconds[name] = false; |
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} else { |
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$negative_preconds[name] = true; |
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$positive_preconds[name] = false; |
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} |
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} |
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void Action::effect(int name, bool val) { |
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if(val) { |
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$positive_effects[name] = true; |
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$negative_effects[name] = false; |
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} else { |
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$negative_effects[name] = true; |
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$positive_effects[name] = false; |
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} |
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} |
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bool Action::can_effect(State& state) { |
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return ((state & $positive_preconds) == $positive_preconds) && |
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((state & $negative_preconds) == ALL_ZERO); |
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} |
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State Action::apply_effect(State& state) { |
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return (state | $positive_effects) & ~$negative_effects; |
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} |
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int distance_to_goal(State& from, State& to) { |
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auto result = from ^ to; |
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return result.count(); |
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} |
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Script reconstruct_path(std::unordered_map<Action, Action>& came_from, Action& current) { |
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Script total_path{current}; |
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int count = 0; |
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while(came_from.contains(current) && count++ < 10) { |
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current = came_from.at(current); |
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if(current != FINAL_ACTION) { |
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total_path.push_front(current); |
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} |
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} |
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return total_path; |
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} |
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inline int h(State& start, State& goal) { |
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return distance_to_goal(start, goal); |
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} |
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inline int d(State& start, State& goal) { |
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return distance_to_goal(start, goal); |
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} |
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ActionState find_lowest(std::unordered_map<ActionState, int>& open_set) { |
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check(!open_set.empty(), "open set can't be empty in find_lowest"); |
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const ActionState *result = nullptr; |
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int lowest_score = SCORE_MAX; |
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for(auto& kv : open_set) { |
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if(kv.second < lowest_score) { |
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lowest_score = kv.second; |
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result = &kv.first; |
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} |
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} |
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return *result; |
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} |
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std::optional<Script> plan_actions(std::vector<Action>& actions, State& start, State& goal) { |
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std::unordered_map<ActionState, int> open_set; |
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std::unordered_map<Action, Action> came_from; |
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std::unordered_map<State, int> g_score; |
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ActionState start_state{FINAL_ACTION, start}; |
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g_score[start] = 0; |
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open_set[start_state] = g_score[start] + h(start, goal); |
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while(!open_set.empty()) { |
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auto current = find_lowest(open_set); |
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if(is_subset(current.state, goal)) { |
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return std::make_optional<Script>(reconstruct_path(came_from, current.action)); |
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} |
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open_set.erase(current); |
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for(auto& neighbor_action : actions) { |
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// calculate the State being current/neighbor
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if(!neighbor_action.can_effect(current.state)) { |
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continue; |
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} |
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auto neighbor = neighbor_action.apply_effect(current.state); |
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int d_score = d(current.state, neighbor); |
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int tentative_g_score = g_score[current.state] + d_score; |
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int neighbor_g_score = g_score.contains(neighbor) ? g_score[neighbor] : SCORE_MAX; |
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if(tentative_g_score < neighbor_g_score) { |
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came_from.insert_or_assign(neighbor_action, current.action); |
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g_score[neighbor] = tentative_g_score; |
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// open_set gets the fScore
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ActionState neighbor_as{neighbor_action, neighbor}; |
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open_set[neighbor_as] = tentative_g_score + h(neighbor, goal); |
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} |
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} |
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} |
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return std::nullopt; |
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} |
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} |
@ -0,0 +1,76 @@ |
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#pragma once |
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#include <vector> |
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#include "matrix.hpp" |
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#include <bitset> |
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#include <limits> |
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#include <optional> |
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#include <nlohmann/json.hpp> |
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#include "config.hpp" |
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namespace ai { |
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constexpr const int SCORE_MAX = std::numeric_limits<int>::max(); |
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constexpr const size_t STATE_MAX = 32; |
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using State = std::bitset<STATE_MAX>; |
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const State ALL_ZERO; |
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const State ALL_ONES = ~ALL_ZERO; |
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struct Action { |
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std::string $name; |
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int $cost = 0; |
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State $positive_preconds; |
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State $negative_preconds; |
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State $positive_effects; |
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State $negative_effects; |
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Action(std::string name, int cost) : |
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$name(name), $cost(cost) { } |
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void needs(int name, bool val); |
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void effect(int name, bool val); |
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bool can_effect(State& state); |
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State apply_effect(State& state); |
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bool operator==(const Action& other) const { |
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return other.$name == $name; |
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} |
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}; |
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using Script = std::deque<Action>; |
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const Action FINAL_ACTION("END", SCORE_MAX); |
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struct ActionState { |
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Action action; |
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State state; |
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ActionState(Action action, State state) : |
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action(action), state(state) {} |
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bool operator==(const ActionState& other) const { |
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return other.action == action && other.state == state; |
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} |
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}; |
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bool is_subset(State& source, State& target); |
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int distance_to_goal(State& from, State& to); |
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std::optional<Script> plan_actions(std::vector<Action>& actions, State& start, State& goal); |
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} |
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template<> struct std::hash<ai::Action> { |
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size_t operator()(const ai::Action& p) const { |
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return std::hash<std::string>{}(p.$name); |
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} |
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}; |
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template<> struct std::hash<ai::ActionState> { |
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size_t operator()(const ai::ActionState& p) const { |
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return std::hash<ai::Action>{}(p.action) ^ std::hash<ai::State>{}(p.state); |
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} |
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}; |
@ -0,0 +1,85 @@ |
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{ |
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"profile": { |
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"target_acquired": 0, |
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"target_lost": 1, |
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"target_in_warhead_range": 2, |
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"target_dead": 3 |
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}, |
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"actions": [ |
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{ |
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"name": "searchSpiral", |
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"cost": 10, |
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"needs": { |
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"target_acquired": false, |
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"target_lost": true |
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}, |
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"effects": { |
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"target_acquired": true |
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} |
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}, |
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{ |
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"name": "searchSerpentine", |
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"cost": 5, |
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"needs": { |
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"target_acquired": false, |
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"target_lost": false |
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}, |
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"effects": { |
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"target_acquired": true |
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} |
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}, |
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{ |
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"name": "searchSpiral", |
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"cost": 5, |
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"needs": { |
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"target_acquired": false, |
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"target_lost": true |
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}, |
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"effects": { |
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"target_acquired": true |
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} |
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}, |
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{ |
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"name": "interceptTarget", |
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"cost": 5, |
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"needs": { |
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"target_acquired": true, |
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"target_dead": false |
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}, |
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"effects": { |
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"target_in_warhead_range": true |
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} |
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}, |
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{ |
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"name": "detonateNearTarget", |
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"cost": 5, |
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"needs": { |
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"target_in_warhead_range": true, |
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"target_acquired": true, |
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"target_dead": false |
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}, |
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"effects": { |
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"target_dead": true |
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} |
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} |
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], |
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"states": { |
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"test_start": { |
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"target_acquired": false, |
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"target_lost": true, |
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"target_in_warhead_range": false, |
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"target_dead": false |
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}, |
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"test_goal": { |
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"target_dead": true |
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} |
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}, |
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"scripts": { |
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"test1": [ |
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"searchSpiral", |
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"searchSerpentine", |
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"searchSpiral", |
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"interceptTarget", |
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"detonateNearTarget"] |
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} |
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} |
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