def conditions(life, alife_seen, alife_not_seen, targets_seen,
targets_not_seen, source_map):
return False
RETURN_VALUE = STATE_UNCHANGED
if not lfe.execute_raw(life, 'state', 'explore'):
return False
if not life['state'] == STATE:
RETURN_VALUE = STATE_CHANGE
#_leading_target = judgement.get_leading_target(life)
#if _leading_target:
# _known = brain.knows_alife_by_id(life, _leading_target)
#
# print _leading_target
_explore_chunk = chunks.find_best_chunk(life, ignore_time=True)
brain.store_in_memory(life, 'explore_chunk', _explore_chunk)
if not _explore_chunk:
return False
return RETURN_VALUE
def tick(life):
if not lfe.execute_raw(life, 'discover', 'discover_type'):
_lost_method = lfe.execute_raw(life, 'discover', 'when_lost')
if _lost_method:
if not life['path'] or not brain.retrieve_from_memory(
life, 'discovery_lock'):
if not 'scanned_chunks' in life['state_flags']:
life['state_flags']['scanned_chunks'] = []
sight.scan_surroundings(
life,
_chunks=brain.get_flag(life, 'visible_chunks'),
ignore_chunks=life['state_flags']['scanned_chunks'])
_explore_chunk = chunks.find_best_chunk(
life,
ignore_starting=True,
ignore_time=True,
lost_method=_lost_method,
only_recent=True)
brain.store_in_memory(life, 'discovery_lock', True)
brain.store_in_memory(life, 'explore_chunk', _explore_chunk)
if not _explore_chunk:
brain.flag(life, 'lost')
return False
survival.explore_known_chunks(life)
else:
return False
def judge(life):
_threats = []
_combat_targets = []
_neutral_targets = []
_tension = 0
for alife_id in life['know']:
_target = life['know'][alife_id]
if _target['escaped'] == 2 or _target['dead'] or not _target['last_seen_at']:
continue
#TODO: Unhardcode 1. Can be used for reaction times
if _target['last_seen_time'] >= 1 and not _target['escaped']:
#TODO: Unhardcode 2000. How long to wait until a target is lost
if _target['last_seen_time'] >= 2000:
_target['escaped'] = 2
logging.debug('%s flagged %s as lost (state 2).' % (' '.join(life['name']), ' '.join(_target['life']['name'])))
elif _target['last_seen_time']>=25 and not _target['escaped']:
_target['escaped'] = 1
_tension += get_tension_with(life, alife_id)
if can_trust(life, alife_id):
_neutral_targets.append(alife_id)
else:
_combat_targets.append(alife_id)
_threats.append(alife_id)
brain.store_in_memory(life, 'threats', _threats)
brain.store_in_memory(life, 'combat_targets', _combat_targets)
brain.store_in_memory(life, 'targets', _neutral_targets)
brain.store_in_memory(life, 'tension_spike', _tension-get_tension(life))
brain.store_in_memory(life, 'tension', _tension)
def conditions(life, alife_seen, alife_not_seen, targets_seen, targets_not_seen, source_map): RETURN_VALUE = STATE_UNCHANGED if not life['state'] == STATE: RETURN_VALUE = STATE_CHANGE _lost_targets = judgement.get_combat_targets(life, escaped_only=True) brain.store_in_memory(life, 'lost_targets', _lost_targets) if not _lost_targets: return False else: print life['name'], _lost_targets return RETURN_VALUE
def conditions(life, alife_seen, alife_not_seen, targets_seen,
targets_not_seen, source_map):
RETURN_VALUE = STATE_UNCHANGED
if not life['state'] == STATE:
RETURN_VALUE = STATE_CHANGE
_lost_targets = judgement.get_combat_targets(life, escaped_only=True)
brain.store_in_memory(life, 'lost_targets', _lost_targets)
if not _lost_targets:
return False
else:
print life['name'], _lost_targets
return RETURN_VALUE
def conditions(life, alife_seen, alife_not_seen, targets_seen, targets_not_seen, source_map): RETURN_VALUE = STATE_UNCHANGED if not judgement.is_safe(life): return False _job = jobs.alife_has_job(life) _active_goals = goals.get_active_goals(life) brain.store_in_memory(life, 'active_goals', value=_active_goals) if not _active_goals and not life['job'] and not _job: return False if not life['state'] == STATE: RETURN_VALUE = STATE_CHANGE return RETURN_VALUE
def tick(life): if not lfe.execute_raw(life, 'discover', 'discover_type'): _lost_method = lfe.execute_raw(life, 'discover', 'when_lost') if _lost_method: if not life['path'] or not brain.retrieve_from_memory(life, 'discovery_lock'): if not 'scanned_chunks' in life['state_flags']: life['state_flags']['scanned_chunks'] = [] sight.scan_surroundings(life, _chunks=brain.get_flag(life, 'visible_chunks'), ignore_chunks=life['state_flags']['scanned_chunks']) _explore_chunk = chunks.find_best_chunk(life, ignore_starting=True, ignore_time=True, lost_method=_lost_method, only_recent=True) brain.store_in_memory(life, 'discovery_lock', True) brain.store_in_memory(life, 'explore_chunk', _explore_chunk) if not _explore_chunk: brain.flag(life, 'lost') return False survival.explore_known_chunks(life) else: return False
def judge(life):
_threats = []
_combat_targets = []
_neutral_targets = []
_tension = 0
for alife_id in life["know"]:
_target = life["know"][alife_id]
if _target["escaped"] == 2 or _target["dead"] or not _target["last_seen_at"]:
continue
# Lost targets
# TODO: Unhardcode 1. Can be used for reaction times
if _target["last_seen_time"] >= 1 and not _target["escaped"]:
# TODO: Unhardcode 2000. How long to wait until a target is lost
# Note that `escaped` can be set to 2 after a failed search
if _target["last_seen_time"] >= 2000:
_target["escaped"] = 2
logging.debug(
"%s flagged %s as lost (state 2)." % (" ".join(life["name"]), " ".join(_target["life"]["name"]))
)
elif _target["last_seen_time"] >= 25 and not _target["escaped"]:
_target["escaped"] = 1
# print 'lost.....'
_tension += get_tension_with(life, alife_id)
_threat = is_target_threat(life, alife_id)
if _threat:
_combat_targets.append(alife_id)
_threats.append(alife_id)
else:
_neutral_targets.append(alife_id)
brain.store_in_memory(life, "threats", _threats)
brain.store_in_memory(life, "combat_targets", _combat_targets)
brain.store_in_memory(life, "targets", _neutral_targets)
brain.store_in_memory(life, "tension_spike", _tension - get_tension(life))
brain.store_in_memory(life, "tension", _tension)
def setup(life):
_needs_to_meet = []
_needs_to_satisfy = []
_needs_unmet = []
for need in life['needs'].values():
if not survival.needs_to_satisfy(life, need):
continue
if survival.can_satisfy(life, need):
_needs_to_satisfy.append(need)
if not survival.can_satisfy(
life, need) and not survival.can_potentially_satisfy(
life, need):
_needs_unmet.append(need)
continue
if not need in _needs_to_satisfy and not need in _needs_to_satisfy:
_needs_to_meet.append(need)
brain.store_in_memory(life, 'needs_to_meet', _needs_to_meet)
brain.store_in_memory(life, 'needs_to_satisfy', _needs_to_satisfy)
brain.store_in_memory(life, 'needs_unmet', _needs_unmet)
if not _needs_to_meet and not _needs_to_satisfy:
return False
def setup(life): _needs_to_meet = [] _needs_to_satisfy = [] _needs_unmet = [] for need in life['needs'].values(): if not survival.needs_to_satisfy(life, need): continue if survival.can_satisfy(life, need): _needs_to_satisfy.append(need) if not survival.can_satisfy(life, need) and not survival.can_potentially_satisfy(life, need): _needs_unmet.append(need) continue if not need in _needs_to_satisfy and not need in _needs_to_satisfy: _needs_to_meet.append(need) brain.store_in_memory(life, 'needs_to_meet', _needs_to_meet) brain.store_in_memory(life, 'needs_to_satisfy', _needs_to_satisfy) brain.store_in_memory(life, 'needs_unmet', _needs_unmet) if not _needs_to_meet and not _needs_to_satisfy: return False
def conditions(life, alife_seen, alife_not_seen, targets_seen, targets_not_seen, source_map):
return False
RETURN_VALUE = STATE_UNCHANGED
if not lfe.execute_raw(life, "state", "explore"):
return False
if not life["state"] == STATE:
RETURN_VALUE = STATE_CHANGE
# _leading_target = judgement.get_leading_target(life)
# if _leading_target:
# _known = brain.knows_alife_by_id(life, _leading_target)
#
# print _leading_target
_explore_chunk = chunks.find_best_chunk(life, ignore_time=True)
brain.store_in_memory(life, "explore_chunk", _explore_chunk)
if not _explore_chunk:
return False
return RETURN_VALUE