diff --git a/main.py b/main.py
index dd9a4e5fbf12a7a067034aef95ff50cb75a9038d..aaa32f5ae20011d0a10392e5c026d09792fc9045 100644
--- a/main.py
+++ b/main.py
@@ -82,11 +82,11 @@ def interpret_user_output(action_id):
return user_action, user_react_time
-def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_value, user_attention_name, user_attention_value,
+def simulation(user_bn_model, user_var_target_action, user_var_target_react_time, user_memory_name, user_memory_value, user_attention_name, user_attention_value,
user_reactivity_name, user_reactivity_value,
task_progress_name, game_attempt_name, robot_assistance_name, robot_feedback_name,
- robot_bn_model, robot_var_target,
- other_user_bn_model, other_user_var_target, other_user_memory_name, other_user_memory_value,
+ robot_bn_model, robot_var_ass_action, robot_var_feedback_action,
+ other_user_bn_model, other_user_var_target_action, other_user_var_target_react_time, other_user_memory_name, other_user_memory_value,
other_user_attention_name, other_user_attention_value,
other_user_reactivity_name, other_user_reactivity_value,
epochs=50, task_complexity=5):
@@ -129,17 +129,8 @@ def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_val
n_timeout_per_episode = [0]*epochs
n_max_attempt_per_episode = [0]*epochs
game_performance_episode = [0]*epochs
- n_lev_0_no_feed_per_episode = [0]*epochs
- n_lev_1_no_feed_per_episode = [0]*epochs
- n_lev_2_no_feed_per_episode = [0]*epochs
- n_lev_3_no_feed_per_episode = [0]*epochs
- n_lev_4_no_feed_per_episode = [0]*epochs
- n_lev_0_with_feed_per_episode = [0]*epochs
- n_lev_1_with_feed_per_episode = [0]*epochs
- n_lev_2_with_feed_per_episode = [0]*epochs
- n_lev_3_with_feed_per_episode = [0]*epochs
- n_lev_4_with_feed_per_episode = [0]*epochs
- robot_assistance_per_episode = [0]*epochs
+ n_assistance_lev_per_episode = [[0 for i in range(Robot_Assistance.counter.value)] for j in range(epochs)]
+ n_feedback_per_episode = [[0 for i in range(Robot_Feedback.counter.value)] for j in range(epochs)]
for e in range(epochs):
'''Simulation framework'''
@@ -151,14 +142,17 @@ def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_val
wrong_move_counter = 0
timeout_counter = 0
max_attempt_counter = 0
- robot_assistance_action = 0
- robot_feedback_action = 0
+ selected_robot_assistance_action = 0
+ selected_robot_feedback_action = 0
- dynamic_variables = {'attempt': attempt_counter_per_action,
+ user_dynamic_variables = {'attempt': attempt_counter_per_action,
'game_state': game_state_counter_per_action,
'robot_assistance': robot_assistance_per_action,
'robot_feedback': robot_feedback_per_action}
+ robot_dynamic_variables = {'attempt': attempt_counter_per_action,
+ 'game_state': game_state_counter_per_action}
+
while(task_evolution<task_complexity):
#if then else are necessary to classify the task game state into beg, mid, end
if task_evolution>=0 and task_evolution<=1:
@@ -168,46 +162,24 @@ def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_val
else:
game_state_counter = 2
- robot_vars_evidence = { user_reactivity_name: user_reactivity_value,
- user_memory_name: user_memory_value,
- task_progress_name: game_state_counter,
- game_attempt_name: attempt_counter,
- }
- robot_actions_prob = bn_functions.infer_prob_from_state(robot_bn_model,
- infer_variable=robot_var_target,
+ robot_vars_evidence = {user_reactivity_name: user_reactivity_value,
+ user_memory_name: user_memory_value,
+ task_progress_name: game_state_counter,
+ game_attempt_name: attempt_counter,
+ }
+ query_robot_ass_prob = bn_functions.infer_prob_from_state(robot_bn_model,
+ infer_variable=robot_var_ass_action,
evidence_variables=robot_vars_evidence)
+ query_robot_feedback_prob = bn_functions.infer_prob_from_state(robot_bn_model,
+ infer_variable=robot_var_feedback_action,
+ evidence_variables=robot_vars_evidence)
- robot_action = bn_functions.get_stochastic_action(robot_actions_prob.values)
- n_robot_assistance_feedback = Robot_Assistance_Feedback.counter.value
- if robot_action>=n_robot_assistance_feedback/2:
- robot_feedback_action = 1
- robot_assistance_action = n_robot_assistance_feedback-robot_action-1
- if robot_assistance_action == 0:
- n_lev_0_no_feed_per_episode[e] += 1
- elif robot_assistance_action == 1:
- n_lev_1_no_feed_per_episode[e] += 1
- elif robot_assistance_action == 2:
- n_lev_2_no_feed_per_episode[e] += 1
- elif robot_assistance_action == 3:
- n_lev_3_no_feed_per_episode[e] += 1
- else:
- n_lev_4_no_feed_per_episode[e] += 1
- else:
- robot_feedback_action = 0
- robot_assistance_action = robot_action
- if robot_assistance_action == 0:
- n_lev_0_with_feed_per_episode[e] += 1
- elif robot_assistance_action == 1:
- n_lev_1_with_feed_per_episode[e] += 1
- elif robot_assistance_action == 2:
- n_lev_2_with_feed_per_episode[e] += 1
- elif robot_assistance_action == 3:
- n_lev_3_with_feed_per_episode[e] += 1
- else:
- n_lev_4_with_feed_per_episode[e] += 1
-
-
- print("robot_assistance {}, attempt {}, game {}, robot_feedback {}".format(robot_assistance_action, attempt_counter, game_state_counter, robot_feedback_action))
+ selected_robot_assistance_action = bn_functions.get_stochastic_action(query_robot_ass_prob)
+ selected_robot_feedback_action = bn_functions.get_stochastic_action(query_robot_feedback_prob)
+
+ n_assistance_lev_per_episode[selected_robot_assistance_action][e] += 1
+ n_feedback_per_episode[selected_robot_feedback_action][e] += 1
+ print("robot_assistance {}, attempt {}, game {}, robot_feedback {}".format(selected_robot_assistance_action, attempt_counter, game_state_counter, selected_robot_feedback_action))
#compare the real user with the estimated Persona and returns a user action (0, 1, 2)
if other_user_bn_model!=None:
@@ -217,12 +189,16 @@ def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_val
other_user_memory_name:other_user_memory_value,
task_progress_name:game_state_counter,
game_attempt_name:attempt_counter,
- robot_assistance_name:robot_assistance_action,
- robot_feedback_name:robot_feedback_action
+ robot_assistance_name:selected_robot_assistance_action,
+ robot_feedback_name:selected_robot_feedback_action
}
- user_actions_prob = bn_functions.infer_prob_from_state(other_user_bn_model,
- infer_variable=other_user_var_target,
- evidence_variables=other_user_vars_evidence)
+ query_user_action_prob = bn_functions.infer_prob_from_state(user_bn_model,
+ infer_variable=user_var_target_action,
+ evidence_variables=user_vars_evidence)
+ query_user_react_time_prob = bn_functions.infer_prob_from_state(user_bn_model,
+ infer_variable=user_var_target_react_time,
+ evidence_variables=user_vars_evidence)
+
else:
#return the user action in this state based on the Persona profile
@@ -232,31 +208,39 @@ def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_val
user_memory_name: user_memory_value,
task_progress_name: game_state_counter,
game_attempt_name: attempt_counter,
- robot_assistance_name: robot_assistance_action,
- robot_feedback_name: robot_feedback_action
+ robot_assistance_name: selected_robot_assistance_action,
+ robot_feedback_name: selected_robot_feedback_action
}
- query_res_prob = bn_functions.infer_prob_from_state(user_bn_model,
- infer_variable=user_var_target,
+ query_user_action_prob = bn_functions.infer_prob_from_state(user_bn_model,
+ infer_variable=user_var_target_action,
+ evidence_variables=user_vars_evidence)
+ query_user_react_time_prob = bn_functions.infer_prob_from_state(user_bn_model,
+ infer_variable=user_var_target_react_time,
evidence_variables=user_vars_evidence)
+
+
#this is needed because we are querying the system with user_react_time and user_action output is 3x3
- query_res_prob_to_list = [query_res_prob.values[i][j] for i in range(query_res_prob.values.shape[0]) for j in range (query_res_prob.values.shape[1])]
- query_action = bn_functions.get_stochastic_action(query_res_prob_to_list)
- user_action, user_react_time = bn_functions.interpret_user_output(query_action)
- #updates counters for plots
- robot_assistance_per_action[user_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
+ selected_user_action = bn_functions.get_stochastic_action(query_user_action_prob)
+ selected_user_react_time = bn_functions.get_stochastic_action(query_user_react_time_prob)
+
+ #updates counters for user and robot model
+ robot_assistance_per_action[selected_user_action][selected_robot_assistance_action] += 1
+ attempt_counter_per_action[selected_user_action][attempt_counter] += 1
+ game_state_counter_per_action[selected_user_action][game_state_counter] += 1
+ robot_feedback_per_action[selected_user_action][selected_robot_feedback_action] += 1
+
+ #TODO create the counters for the user_react_time variables
#updates counters for simulation
iter_counter += 1
task_evolution, attempt_counter, correct_move_counter, \
- wrong_move_counter, timeout_counter, max_attempt_counter = compute_next_state(user_action,
+ wrong_move_counter, timeout_counter, max_attempt_counter = compute_next_state(selected_user_action,
task_evolution, attempt_counter,
correct_move_counter, wrong_move_counter,
timeout_counter, max_attempt_counter)
+ ####################################END of EPISODE#######################################
print("task_evolution {}, attempt_counter {}, timeout_counter {}".format(task_evolution, iter_counter, timeout_counter))
print("robot_assistance_per_action {}".format(robot_assistance_per_action))
print("attempt_counter_per_action {}".format(attempt_counter_per_action))
@@ -265,17 +249,20 @@ def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_val
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))
+ #update user model
+ user_bn_model = bn_functions.update_cpds_tables(user_bn_model, user_dynamic_variables)
+ #update robot model
+ robot_bn_model = bn_functions.update_cpds_tables(robot_bn_model, robot_dynamic_variables)
- user_bn_model = bn_functions.update_cpds_tables(user_bn_model, dynamic_variables)
- #reset counter??
+ #reset counter
robot_assistance_per_action = [[0 for i in range(Robot_Assistance.counter.value)] for j in
range(User_Action.counter.value)]
- attempt_counter_per_action = [[0 for i in range(Attempt.counter.value)] for j in
- range(User_Action.counter.value)]
- game_state_counter_per_action = [[0 for i in range(Game_State.counter.value)] for j in
- range(User_Action.counter.value)]
robot_feedback_per_action = [[0 for i in range(Robot_Feedback.counter.value)] for j in
range(User_Action.counter.value)]
+ game_state_counter_per_action = [[0 for i in range(Game_State.counter.value)] for j in
+ range(User_Action.counter.value)]
+ attempt_counter_per_action = [[0 for i in range(Attempt.counter.value)] for j in
+ range(User_Action.counter.value)]
#for plots
n_correct_per_episode[e] = correct_move_counter
@@ -286,15 +273,9 @@ def simulation(user_bn_model, user_var_target, user_memory_name, user_memory_val
n_wrong_per_episode[e],
n_timeout_per_episode[e],
n_max_attempt_per_episode[e]]
- robot_assistance_per_episode[e] = [n_lev_0_no_feed_per_episode[e],
- n_lev_1_no_feed_per_episode[e], n_lev_2_no_feed_per_episode[e],
- n_lev_3_no_feed_per_episode[e], n_lev_4_no_feed_per_episode[e],
- n_lev_0_with_feed_per_episode[e], n_lev_1_with_feed_per_episode[e],
- n_lev_2_with_feed_per_episode[e], n_lev_3_with_feed_per_episode[e],
- n_lev_4_with_feed_per_episode[e]
- ]
- return game_performance_episode, robot_assistance_per_episode
+
+ return game_performance_episode, n_assistance_lev_per_episode, n_feedback_per_episode
@@ -320,14 +301,19 @@ persona_cpds = bnlearn.import_DAG('bn_persona_model/new_persona_model.bif')
real_user_memory = 2; real_user_attention = 2; real_user_reactivity = 2;
real_user_cpds = None#bnlearn.import_DAG('bn_other_user_model/user_model.bif')
-game_performance_per_episode, robot_assistance_per_episode = simulation(user_bn_model=persona_cpds, user_var_target=['user_action', 'user_react_time'], user_memory_name="memory", user_memory_value=persona_memory,
- user_attention_name="attention", user_attention_value=persona_attention,
+game_performance_per_episode, robot_assistance_per_episode = \
+ simulation(user_bn_model=persona_cpds, user_var_target_action=['user_action'],
+ user_var_target_react_time=['user_react_time'],
+ user_memory_name="memory", user_memory_value=persona_memory,
+ user_attention_name="attention", user_attention_value=persona_attention,
user_reactivity_name="reactivity", user_reactivity_value=persona_reactivity,
- task_progress_name="game_state", game_attempt_name="attempt",
- robot_assistance_name="robot_assistance", robot_feedback_name="robot_feedback",
+ task_progress_name="game_state", game_attempt_name="attempt",
+ robot_assistance_name="robot_assistance", robot_feedback_name="robot_feedback",
- robot_bn_model=robot_cpds, robot_var_target=["robot_assistance_feedback"],
- other_user_bn_model=real_user_cpds, other_user_var_target=['user_action'],
+ robot_bn_model=robot_cpds, robot_var_ass_action=["robot_assistance"],
+ robot_var_feedback_action=["robot_feedback"],
+ other_user_bn_model=real_user_cpds, other_user_var_target_action=['user_action'],
+ other_user_var_target_react_time=["user_react_time"],
other_user_memory_name="memory", other_user_memory_value=real_user_memory,
other_user_attention_name="attention", other_user_attention_value=real_user_attention,
other_user_reactivity_name="reactivity", other_user_reactivity_value=real_user_reactivity,