def test_mouse(self, output=False):
agent = Agent(output=output)
env = Environment()
vision = Vision(agent, env.display)
motor = Motor(agent, vision, env)
self.button = None
end = 20.0
def update():
if agent.time() < end:
def fn():
vision.clear()
agent.wait(1.0)
self.button = env.display.add_button(
random.randint(0, 500), random.randint(0, 500), 'X')
agent.run_thread(fn)
update()
def fn(visual):
if visual.obj == 'X':
update()
env.mouse.add_click_fn(fn)
while agent.time() < end:
visual = vision.wait_for(isa='button')
motor.point_and_click(visual)
agent.wait_for_all()
self.assertGreaterEqual(agent.time(), end)
def test_mouse(self, output=False):
agent = Agent(output=output)
vision = Vision(agent)
hands = Hands(agent)
mouse = Mouse(hands, vision)
self.button = None
end = 20.0
def update():
if agent.time() < end:
def fn():
vision.clear()
agent.wait(1.0)
self.button = Visual(random.randint(0, 500),
random.randint(0, 500), 30, 30,
'button')
vision.add(self.button, "X")
agent.run_thread(fn)
update()
def fn(visual):
if visual.equals(self.button):
update()
mouse.add_click_fn(fn)
while agent.time() < end:
visual = vision.wait_for(isa='button')
mouse.point_and_click(visual)
agent.wait_for_all()
self.assertGreaterEqual(agent.time(), end)
def run_trial(self):
agent = Agent(output=False)
memory = Memory(agent, Memory.OPTIMIZED_DECAY)
memory.decay_rate = .5
memory.activation_noise = .5
memory.retrieval_threshold = -1.8
memory.latency_factor = .450
vision = Vision(agent)
typing = Typing(Hands(agent))
self.trial_start = 0
self.block_index = 0
def fn():
for i in range(PairedAssociatesTest.N_BLOCKS):
self.block_index = i
pairs = PairedAssociatesTest.PAIRS.copy()
random.shuffle(pairs)
for pair in pairs:
self.trial_start = agent.time()
vision.clear().add(Visual(50, 50, 20, 20, 'word'), pair[0])
agent.wait(5.0)
vision.clear().add(
Visual(50, 50, 20, 20, 'digit'), pair[1])
agent.wait(5.0)
agent.run_thread(fn)
def type_fn(c):
self.rt.add(self.block_index, agent.time() - self.trial_start)
typing.add_type_fn(type_fn)
for i in range(PairedAssociatesTest.N_BLOCKS):
for _ in range(len(PairedAssociatesTest.PAIRS)):
word = vision.encode(vision.wait_for(isa='word'))
chunk = memory.recall(word=word)
if chunk:
typing.type(chunk.get('digit'))
self.correct.add(i, 1)
else:
self.correct.add(i, 0)
digit = vision.encode(vision.wait_for(isa='digit'))
memory.store(word=word, digit=digit)
agent.wait_for_all()
class VSAgent(Agent):
def __init__(self, env):
"""Initializes the agent"""
super().__init__(output=True)
self.vision = Vision(self, env.display) #, eyes=Eyes(self))
self.motor = Motor(self, self.vision, env)
def run(self, time):
while self.time() < time:
self.vision.wait_for(isa='letter', seen=False)
visual = self.vision.search_for(
Query(isa='letter', color='red', region='vs'), 'X')
if visual:
# self.log('target present')
self.motor.type('w')
self.wait(1.0)
else:
# self.log('target absent')
self.motor.type('r')
self.wait(1.0)
class PVTAgent(Agent):
def __init__(self):
"""Initializes the agent"""
super().__init__(output=True)
self.vision = Vision(self)
self.audition = Audition(self)
self.typing = Typing(Hands(self))
def run(self, time=300):
while self.time() < time:
visual = self.vision.wait_for(seen=False)
self.vision.start_encode(visual)
self.typing.type('j')
self.vision.get_encoded()
class PVTAgent(Agent):
def __init__(self, env):
"""Initializes the agent"""
super().__init__(output=True)
self.vision = Vision(self, env.display) #, eyes=Eyes(self))
self.motor = Motor(self, self.vision, env)
def run(self, time=60):
while self.time() < time:
visual = self.vision.wait_for(isa='letter',
region='pvt',
seen=False)
self.vision.start_encode(visual)
self.motor.type(' ')
self.vision.get_encoded()
class SearchAgent(Agent):
def __init__(self):
super().__init__(output=True)
self.vision = Vision(self)
self.audition = Audition(self)
self.typing = Typing(Hands(self))
def run(self, time=300):
while self.time() < time:
visual = self.vision.wait_for(seen=False)
while (visual is not None
and not visual.isa == 'vertical-line'):
obj = self.vision.encode(visual)
print('**** skip')
visual = self.vision.find(seen=False)
if visual:
print('**** found')
self.typing.type('j')
self.vision.encode(visual)
else:
print('**** not found')
self.typing.type('k')
class UndifferentiatedAgent(Agent):
def __init__(self, env, output=True):
super().__init__(output=output)
#basic pass-ins for now for speed of testing
self.memory = OntologyMemory(self,stopOldServer=False,owlFile='uagent.owl')
self.vision = Vision(self, env.display)
self.audition = Audition(self, env.speakers)
self.motor = Motor(self, self.vision, env)
self.interpreter = Interpreter(self.memory)
self.language = Language(self)
self.language.add_interpreter(lambda words:
self.interpreter.interpret_ace(' '.join(words)))
self.condition_handler = ConditionHandler(self)
self.action_handler = ActionHandler(self)
# #Not used at the moment.
# self.item_role_list = ['target','stimulus','distractor','responseButton','infoButton']
# #"ItemRole" in the Ontology.
# #For future implementations (trying to use other labs' instructions)
# self.agent_synonym_list = ['subject','participant','you']
def is_action(self, rule):
for action in rule.actions:
if self.action_handler._has(action['name']):
return True
return False
def check_condition(self, cond, context):
handler = self.condition_handler._get(cond['name'])
if handler:
self.think('check condition "{}"'.format(cond))
return handler(cond, context)
else:
return True
def execute_action(self, action, context):
handler = self.action_handler._get(action['name'])
if handler:
self.think('execute action "{}"'.format(action))
handler(action, context)
def process_rule(self, rule, context):
if self.is_action(rule):
self.think('process rule "{}"'.format(rule))
for cond in rule.conditions:
if not self.check_condition(cond, context):
return False
for action in rule.actions:
self.execute_action(action, context)
return True
def run(self, time=60):
instr_visual = self.vision.wait_for(isa='text')
instructions = self.vision.encode(instr_visual)
self.language.interpret(instructions)
while self.time() < time:
context = Chunk()
for rule in self.memory.recall_ground_rules():
self.process_rule(rule, context)