diff --git a/main.py b/main.py
index a8063c5c0dbf5a9301c320cf8dc71bb2dda48cd0..659e45b0f21c8d8a32b91870d3986354ce2d02cd 100644
--- a/main.py
+++ b/main.py
@@ -50,11 +50,6 @@ class Attempt(enum.Enum):
name = "attempt"
counter = 4
-model = bnlearn.import_DAG('persona_model_4.bif')
-print("user_action -> attempt ", model['model'].cpds[0].values)
-print("user_action -> game_state ", model['model'].cpds[2].values)
-print("robot_feedback -> robot_assistance ", model['model'].cpds[5].values)
-print("user_action -> reactivity, memory ", model['model'].cpds[6].values)
def plot2D(save_path, n_episodes, *y):
@@ -132,7 +127,7 @@ def generate_user_action(actions_prob):
return action_id
-def simulation(robot_assistance_vect, robot_feedback_vect, memory, attention, reactivity, epochs=50, non_stochastic=False):
+def simulation(robot_assistance_vect, robot_feedback_vect, persona_cpds, memory, attention, reactivity, epochs=50, task_complexity=5, non_stochastic=False):
#metrics we need in order to compute the afterwords the belief
'''
CPD 0: for each attempt 1 to 4 store the number of correct, wrong and timeout
@@ -160,16 +155,16 @@ def simulation(robot_assistance_vect, robot_feedback_vect, memory, attention, re
for e in range(epochs):
'''Simulation framework'''
- task_complexity = 5
+ #counters
task_evolution = 0
-
attempt_counter = 0
- game_state_counter = 0
iter_counter = 0
correct_move_counter = 0
wrong_move_counter = 0
timeout_counter = 0
+
while(task_evolution<=task_complexity):
+ #these, if then else are necessary to classify the task game state into beg, mid, end
if task_evolution>=0 and task_evolution<=2:
game_state_counter = 0
elif task_evolution>=3 and task_evolution<=4:
@@ -182,7 +177,7 @@ def simulation(robot_assistance_vect, robot_feedback_vect, memory, attention, re
robot_feedback_action = random.randint(min(robot_feedback_vect), max(robot_feedback_vect))
print("robot_assistance {}, attempt {}, game {}, robot_feedback {}".format(robot_assistance_action, attempt_counter, game_state_counter, robot_feedback_action))
- query = bnlearn.inference.fit(model, variables=['user_action'], evidence={'robot_assistance': robot_assistance_action,
+ query = bnlearn.inference.fit(persona_cpds, variables=['user_action'], evidence={'robot_assistance': robot_assistance_action,
'attempt': attempt_counter,
'game_state': game_state_counter,
'robot_feedback': robot_feedback_action,
@@ -193,22 +188,27 @@ def simulation(robot_assistance_vect, robot_feedback_vect, memory, attention, re
#generate a random number and trigger one of the three possible action
user_action = generate_user_action(query.values)#np.argmax(query.values, axis=0)
+ #updates counters for plots
robot_assistance_per_feedback[robot_feedback_action][robot_assistance_action] += 1
attempt_counter_per_action[user_action][attempt_counter] += 1
game_state_counter_per_action[user_action][game_state_counter] += 1
robot_feedback_per_action[user_action][robot_feedback_action] += 1
+ #updates counters for simulation
iter_counter += 1
if user_action == 0:
attempt_counter = 0
task_evolution += 1
correct_move_counter += 1
+ #if the user made a wrong move and still did not reach the maximum number of attempts
elif user_action == 1 and attempt_counter<3:
attempt_counter += 1
wrong_move_counter += 1
+ # if the user did not move any token and still did not reach the maximum number of attempts
elif user_action == 2 and attempt_counter<3:
attempt_counter += 1
wrong_move_counter += 1
+ # the robot or therapist makes the correct move on the patient behalf
else:
attempt_counter = 0
task_evolution += 1
@@ -223,25 +223,27 @@ def simulation(robot_assistance_vect, robot_feedback_vect, memory, attention, re
print("iter {}, correct {}, wrong {}, timeout {}".format(iter_counter, correct_move_counter, wrong_move_counter, timeout_counter))
print("correct_move {}, wrong_move {}, timeout {}".format(correct_move_counter, wrong_move_counter, timeout_counter))
+
#transform counters into probabilities
prob_over_attempt_per_action = compute_prob(attempt_counter_per_action)
prob_over_game_per_action = compute_prob(game_state_counter_per_action)
prob_over_feedback_per_action = compute_prob(robot_feedback_per_action)
prob_over_assistance_per_feedback = compute_prob(robot_assistance_per_feedback)
+
#average the probabilities obtained with the cpdf tables
- updated_prob_over_attempt_per_action = average_prob(np.transpose(model['model'].cpds[0].values),
+ updated_prob_over_attempt_per_action = average_prob(np.transpose(persona_cpds['model'].cpds[0].values),
prob_over_attempt_per_action)
- updated_prob_over_game_per_action = average_prob(np.transpose(model['model'].cpds[2].values),
+ updated_prob_over_game_per_action = average_prob(np.transpose(persona_cpds['model'].cpds[2].values),
prob_over_game_per_action)
- updated_prob_over_feedback_per_action = average_prob(np.transpose(model['model'].cpds[6].values),
+ updated_prob_over_feedback_per_action = average_prob(np.transpose(persona_cpds['model'].cpds[6].values),
prob_over_feedback_per_action)
- updated_prob_over_assistance_per_feedback = average_prob(np.transpose(model['model'].cpds[5].values),
+ updated_prob_over_assistance_per_feedback = average_prob(np.transpose(persona_cpds['model'].cpds[5].values),
prob_over_assistance_per_feedback)
- model['model'].cpds[0].values = np.transpose(updated_prob_over_attempt_per_action)
- model['model'].cpds[2].values = np.transpose(updated_prob_over_game_per_action)
- model['model'].cpds[6].values = np.transpose(updated_prob_over_feedback_per_action)
- model['model'].cpds[5].values = np.transpose(updated_prob_over_assistance_per_feedback)
+ persona_cpds['model'].cpds[0].values = np.transpose(updated_prob_over_attempt_per_action)
+ persona_cpds['model'].cpds[2].values = np.transpose(updated_prob_over_game_per_action)
+ persona_cpds['model'].cpds[6].values = np.transpose(updated_prob_over_feedback_per_action)
+ persona_cpds['model'].cpds[5].values = np.transpose(updated_prob_over_assistance_per_feedback)
n_correct_per_episode[e] = correct_move_counter
n_wrong_per_episode[e] = wrong_move_counter
@@ -252,8 +254,16 @@ def simulation(robot_assistance_vect, robot_feedback_vect, memory, attention, re
robot_assistance = [i for i in range(Robot_Assistance.counter.value)]
robot_feedback = [i for i in range(Robot_Feedback.counter.value)]
epochs = 10
+#initialise memory, attention and reactivity varibles
memory = 0; attention = 0; reactivity = 1;
-results = simulation(robot_assistance, robot_feedback, memory, attention, reactivity, 10)
+#run a simulation
+persona_cpds = bnlearn.import_DAG('persona_model.bif')
+print("user_action -> attempt ", persona_cpds['model'].cpds[0].values)
+print("user_action -> game_state ", persona_cpds['model'].cpds[2].values)
+print("robot_feedback -> robot_assistance ", persona_cpds['model'].cpds[5].values)
+print("user_action -> reactivity, memory ", persona_cpds['model'].cpds[6].values)
+
+results = simulation(robot_assistance, robot_feedback, persona_cpds, memory, attention, reactivity, epochs=10, task_complexity=5, non_stochastic=False)
plot_path = "epoch_"+str(epochs)+"_memory_"+str(memory)+"_attention_"+str(attention)+"_reactivity_"+str(reactivity)+".jpg"
plot2D(plot_path, epochs, results)
diff --git a/persona_model_4.bif b/persona_model.bif
similarity index 100%
rename from persona_model_4.bif
rename to persona_model.bif