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
@ -0,0 +1,127 @@ |
||||
#include "dbc.hpp" |
||||
#include "goap.hpp" |
||||
|
||||
namespace ai { |
||||
using namespace nlohmann; |
||||
using namespace dbc; |
||||
|
||||
bool is_subset(State& source, State& target) { |
||||
State result = source & target; |
||||
return result == target; |
||||
} |
||||
|
||||
void Action::needs(int name, bool val) { |
||||
if(val) { |
||||
$positive_preconds[name] = true; |
||||
$negative_preconds[name] = false; |
||||
} else { |
||||
$negative_preconds[name] = true; |
||||
$positive_preconds[name] = false; |
||||
} |
||||
} |
||||
|
||||
void Action::effect(int name, bool val) { |
||||
if(val) { |
||||
$positive_effects[name] = true; |
||||
$negative_effects[name] = false; |
||||
} else { |
||||
$negative_effects[name] = true; |
||||
$positive_effects[name] = false; |
||||
} |
||||
} |
||||
|
||||
|
||||
bool Action::can_effect(State& state) { |
||||
return ((state & $positive_preconds) == $positive_preconds) && |
||||
((state & $negative_preconds) == ALL_ZERO); |
||||
} |
||||
|
||||
State Action::apply_effect(State& state) { |
||||
return (state | $positive_effects) & ~$negative_effects; |
||||
} |
||||
|
||||
int distance_to_goal(State& from, State& to) { |
||||
auto result = from ^ to; |
||||
return result.count(); |
||||
} |
||||
|
||||
Script reconstruct_path(std::unordered_map<Action, Action>& came_from, Action& current) { |
||||
Script total_path{current}; |
||||
int count = 0; |
||||
|
||||
while(came_from.contains(current) && count++ < 10) { |
||||
current = came_from.at(current); |
||||
if(current != FINAL_ACTION) { |
||||
total_path.push_front(current); |
||||
} |
||||
} |
||||
|
||||
return total_path; |
||||
} |
||||
|
||||
inline int h(State& start, State& goal) { |
||||
return distance_to_goal(start, goal); |
||||
} |
||||
|
||||
inline int d(State& start, State& goal) { |
||||
return distance_to_goal(start, goal); |
||||
} |
||||
|
||||
ActionState find_lowest(std::unordered_map<ActionState, int>& open_set) { |
||||
check(!open_set.empty(), "open set can't be empty in find_lowest"); |
||||
const ActionState *result = nullptr; |
||||
int lowest_score = SCORE_MAX; |
||||
|
||||
for(auto& kv : open_set) { |
||||
if(kv.second < lowest_score) { |
||||
lowest_score = kv.second; |
||||
result = &kv.first; |
||||
} |
||||
} |
||||
|
||||
return *result; |
||||
} |
||||
|
||||
std::optional<Script> plan_actions(std::vector<Action>& actions, State& start, State& goal) { |
||||
std::unordered_map<ActionState, int> open_set; |
||||
std::unordered_map<Action, Action> came_from; |
||||
std::unordered_map<State, int> g_score; |
||||
|
||||
ActionState start_state{FINAL_ACTION, start}; |
||||
|
||||
g_score[start] = 0; |
||||
open_set[start_state] = g_score[start] + h(start, goal); |
||||
|
||||
while(!open_set.empty()) { |
||||
auto current = find_lowest(open_set); |
||||
|
||||
if(is_subset(current.state, goal)) { |
||||
return std::make_optional<Script>(reconstruct_path(came_from, current.action)); |
||||
} |
||||
|
||||
open_set.erase(current); |
||||
|
||||
for(auto& neighbor_action : actions) { |
||||
// calculate the State being current/neighbor
|
||||
if(!neighbor_action.can_effect(current.state)) { |
||||
continue; |
||||
} |
||||
|
||||
auto neighbor = neighbor_action.apply_effect(current.state); |
||||
int d_score = d(current.state, neighbor); |
||||
int tentative_g_score = g_score[current.state] + d_score; |
||||
int neighbor_g_score = g_score.contains(neighbor) ? g_score[neighbor] : SCORE_MAX; |
||||
if(tentative_g_score < neighbor_g_score) { |
||||
came_from.insert_or_assign(neighbor_action, current.action); |
||||
|
||||
g_score[neighbor] = tentative_g_score; |
||||
// open_set gets the fScore
|
||||
ActionState neighbor_as{neighbor_action, neighbor}; |
||||
open_set[neighbor_as] = tentative_g_score + h(neighbor, goal); |
||||
} |
||||
} |
||||
} |
||||
|
||||
return std::nullopt; |
||||
} |
||||
} |
||||
@ -0,0 +1,76 @@ |
||||
#pragma once |
||||
#include <vector> |
||||
#include "matrix.hpp" |
||||
#include <bitset> |
||||
#include <limits> |
||||
#include <optional> |
||||
#include <nlohmann/json.hpp> |
||||
#include "config.hpp" |
||||
|
||||
namespace ai { |
||||
constexpr const int SCORE_MAX = std::numeric_limits<int>::max(); |
||||
constexpr const size_t STATE_MAX = 32; |
||||
|
||||
using State = std::bitset<STATE_MAX>; |
||||
|
||||
const State ALL_ZERO; |
||||
const State ALL_ONES = ~ALL_ZERO; |
||||
|
||||
struct Action { |
||||
std::string $name; |
||||
int $cost = 0; |
||||
|
||||
State $positive_preconds; |
||||
State $negative_preconds; |
||||
|
||||
State $positive_effects; |
||||
State $negative_effects; |
||||
|
||||
Action(std::string name, int cost) : |
||||
$name(name), $cost(cost) { } |
||||
|
||||
void needs(int name, bool val); |
||||
void effect(int name, bool val); |
||||
|
||||
bool can_effect(State& state); |
||||
State apply_effect(State& state); |
||||
|
||||
bool operator==(const Action& other) const { |
||||
return other.$name == $name; |
||||
} |
||||
}; |
||||
|
||||
using Script = std::deque<Action>; |
||||
|
||||
const Action FINAL_ACTION("END", SCORE_MAX); |
||||
|
||||
struct ActionState { |
||||
Action action; |
||||
State state; |
||||
|
||||
ActionState(Action action, State state) : |
||||
action(action), state(state) {} |
||||
|
||||
bool operator==(const ActionState& other) const { |
||||
return other.action == action && other.state == state; |
||||
} |
||||
}; |
||||
|
||||
bool is_subset(State& source, State& target); |
||||
|
||||
int distance_to_goal(State& from, State& to); |
||||
|
||||
std::optional<Script> plan_actions(std::vector<Action>& actions, State& start, State& goal); |
||||
} |
||||
|
||||
template<> struct std::hash<ai::Action> { |
||||
size_t operator()(const ai::Action& p) const { |
||||
return std::hash<std::string>{}(p.$name); |
||||
} |
||||
}; |
||||
|
||||
template<> struct std::hash<ai::ActionState> { |
||||
size_t operator()(const ai::ActionState& p) const { |
||||
return std::hash<ai::Action>{}(p.action) ^ std::hash<ai::State>{}(p.state); |
||||
} |
||||
}; |
||||
@ -0,0 +1,85 @@ |
||||
{ |
||||
"profile": { |
||||
"target_acquired": 0, |
||||
"target_lost": 1, |
||||
"target_in_warhead_range": 2, |
||||
"target_dead": 3 |
||||
}, |
||||
"actions": [ |
||||
{ |
||||
"name": "searchSpiral", |
||||
"cost": 10, |
||||
"needs": { |
||||
"target_acquired": false, |
||||
"target_lost": true |
||||
}, |
||||
"effects": { |
||||
"target_acquired": true |
||||
} |
||||
}, |
||||
{ |
||||
"name": "searchSerpentine", |
||||
"cost": 5, |
||||
"needs": { |
||||
"target_acquired": false, |
||||
"target_lost": false |
||||
}, |
||||
"effects": { |
||||
"target_acquired": true |
||||
} |
||||
}, |
||||
{ |
||||
"name": "searchSpiral", |
||||
"cost": 5, |
||||
"needs": { |
||||
"target_acquired": false, |
||||
"target_lost": true |
||||
}, |
||||
"effects": { |
||||
"target_acquired": true |
||||
} |
||||
}, |
||||
{ |
||||
"name": "interceptTarget", |
||||
"cost": 5, |
||||
"needs": { |
||||
"target_acquired": true, |
||||
"target_dead": false |
||||
}, |
||||
"effects": { |
||||
"target_in_warhead_range": true |
||||
} |
||||
}, |
||||
{ |
||||
"name": "detonateNearTarget", |
||||
"cost": 5, |
||||
"needs": { |
||||
"target_in_warhead_range": true, |
||||
"target_acquired": true, |
||||
"target_dead": false |
||||
}, |
||||
"effects": { |
||||
"target_dead": true |
||||
} |
||||
} |
||||
], |
||||
"states": { |
||||
"test_start": { |
||||
"target_acquired": false, |
||||
"target_lost": true, |
||||
"target_in_warhead_range": false, |
||||
"target_dead": false |
||||
}, |
||||
"test_goal": { |
||||
"target_dead": true |
||||
} |
||||
}, |
||||
"scripts": { |
||||
"test1": [ |
||||
"searchSpiral", |
||||
"searchSerpentine", |
||||
"searchSpiral", |
||||
"interceptTarget", |
||||
"detonateNearTarget"] |
||||
} |
||||
} |
||||
Reference in new issue