القائمة
Two-level group-then-pattern dispatch for game AI agents where groups filter by game state and ordered patterns within a group fire the first matching action
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استنادا إلى تصنيف SOC المهني
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| name | tabular-hierarchical-rule-engine |
| description | Two-level group-then-pattern dispatch for game AI agents where groups filter by game state and ordered patterns within a group fire the first matching action |
For heuristic game AI agents, a flat if-else chain becomes unmaintainable past ~20 rules. A two-level dispatch — groups that test broad game state (attacking, defending, set piece), then ordered patterns within the matching group — scales to hundreds of rules while staying readable. Each pattern is a dict with environment_fits (predicate) and get_action (callback), evaluated in priority order.
def make_pattern(predicate, action_fn):
return lambda obs, x, y: {
"environment_fits": predicate,
"get_action": action_fn,
}
def make_group(group_predicate, patterns_fn):
return lambda obs, x, y: {
"environment_fits": group_predicate,
"get_memory_patterns": patterns_fn,
}
groups = [attack_group, defend_group, set_piece_group, fallback_group]
def get_action(obs, x, y):
for make_grp in groups:
grp = make_grp(obs, x, y)
if grp["environment_fits"](obs, x, y):
for make_pat in grp["get_memory_patterns"](obs, x, y):
pat = make_pat(obs, x, y)
if pat["environment_fits"](obs, x, y):
return pat["get_action"](obs, x, y)
return default_action
environment_fits) and action callback