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Gave up on trying to get the GOAP algorithm to correctly apply the cost structure to competing choices, and instead I take the resulting action list and simply find the next best one based on cost.

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Zed A. Shaw 2025-04-01 13:48:59 -04:00
parent 52f45e1d45
commit c014e65c13
11 changed files with 43 additions and 30 deletions
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11
goap.cpp
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@ -40,8 +40,9 @@ namespace ai {
}
bool Action::can_effect(State& state) {
return ((state & $positive_preconds) == $positive_preconds) &&
((state & $negative_preconds) == ALL_ZERO);
bool posbit_match = (state & $positive_preconds) == $positive_preconds;
bool negbit_match = (state & $negative_preconds) == ALL_ZERO;
return posbit_match && negbit_match;
}
State Action::apply_effect(State& state) {
@ -113,11 +114,11 @@ namespace ai {
ActionState find_lowest(std::unordered_map<ActionState, int>& open_set) {
check(!open_set.empty(), "open set can't be empty in find_lowest");
int found_score = SCORE_MAX;
int found_score = std::numeric_limits<int>::max();
ActionState found_as;
for(auto& kv : open_set) {
if(kv.second < found_score) {
if(kv.second <= found_score) {
found_score = kv.second;
found_as = kv.first;
}
@ -166,7 +167,7 @@ namespace ai {
g_score.insert_or_assign(neighbor, tentative_g_score);
ActionState neighbor_as{neighbor_action, neighbor};
int score = tentative_g_score + h(neighbor, goal) + neighbor_action.cost;
int score = tentative_g_score + h(neighbor, goal);
// this maybe doesn't need score
open_set.insert_or_assign(neighbor_as, score);