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Commit 9a38c6df authored by Antonio Andriella's avatar Antonio Andriella
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new code with 4 BNs

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import bnlearn
import os
#import classes and modules
from bn_variables import Memory, Attention, Reactivity, Robot_Assistance, Robot_Feedback, Robot_Assistance_Feedback, User_Action, User_Capability, Game_State, Attempt
from bn_variables import Robot_Assistance, Robot_Feedback, User_Action, User_React_time, Game_State, Attempt
import bn_functions
import utils
......@@ -47,49 +47,15 @@ def compute_next_state(user_action, task_evolution, attempt_counter, correct_mov
return task_evolution, attempt_counter, correct_move_counter, wrong_move_counter, timeout_counter, max_attept_counter
def interpret_user_output(action_id):
user_action = 0
user_react_time = 0
if action_id == 0:
user_action = 0;
user_react_time = 0
elif action_id == 1:
user_action = 1;
user_react_time = 0
elif action_id == 2:
user_action = 2;
user_react_time = 0
elif action_id == 3:
user_action = 0;
user_react_time = 1
elif action_id == 4:
user_action = 1;
user_react_time = 1
elif action_id == 5:
user_action = 2;
user_react_time = 1
elif action_id == 6:
user_action = 0;
user_react_time = 2
elif action_id == 7:
user_action = 1;
user_react_time = 2
elif action_id == 8:
user_action = 2;
user_react_time = 2
return user_action, user_react_time
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,
def simulation(bn_model_user_action, var_user_action_target_action, bn_model_user_react_time, var_user_react_time_target_action,
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_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):
bn_model_robot_assistance, var_robot_assistance_target_action, bn_model_robot_feedback,
var_robot_feedback_target_action, other_user_bn_model, var_other_user_action_target_action,
var_other_user_target_react_time_action, 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):
'''
Args:
......@@ -102,26 +68,23 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
#TODO: remove robot_assistance_vect and robot_feedback_vect
#metrics we need, in order to compute afterwords the belief
'''
CPD 0: for each attempt 1 to 4 store the number of correct, wrong and timeout
'''
attempt_counter_per_action = [[0 for i in range(Attempt.counter.value)] for j in range(User_Action.counter.value)]
'''
CPD 2: for each game_state 0 to 2 store the number of correct, wrong and timeout
'''
game_state_counter_per_action = [[0 for i in range(Game_State.counter.value)] for j in range(User_Action.counter.value)]
'''
CPD 5: for each robot feedback store the number of correct, wrong and timeout
'''
robot_feedback_per_action = [[0 for i in range(Robot_Feedback.counter.value)] for j in range(User_Action.counter.value)]
'''
CPD 6: for each robot assistance store the number of pos and neg feedback
'''
robot_assistance_per_action = [[0 for i in range(Robot_Assistance.counter.value)] for j in range(User_Action.counter.value)]
attempt_counter_per_react_time = [[0 for i in range(Attempt.counter.value)] for j in range(User_React_time.counter.value)]
game_state_counter_per_react_time = [[0 for i in range(Game_State.counter.value)] for j in range(User_React_time.counter.value)]
robot_feedback_per_react_time = [[0 for i in range(Robot_Feedback.counter.value)] for j in range(User_React_time.counter.value)]
robot_assistance_per_react_time = [[0 for i in range(Robot_Assistance.counter.value)] for j in range(User_React_time.counter.value)]
game_state_counter_per_robot_assistance = [[0 for i in range(Game_State.counter.value)] for j in range(Robot_Assistance.counter.value)]
attempt_counter_per_robot_assistance = [[0 for i in range(Attempt.counter.value)] for j in range(Robot_Assistance.counter.value)]
game_state_counter_per_robot_feedback = [[0 for i in range(Game_State.counter.value)] for j in range(Robot_Feedback.counter.value)]
attempt_counter_per_robot_feedback = [[0 for i in range(Attempt.counter.value)] for j in range(Robot_Feedback.counter.value)]
#these are the variables of the persona bn that are dynamic and will be affected from the game evolution
#TODO: it might be worth to integrate them as a param in the simulation function, only the name?
#output variables:
n_correct_per_episode = [0]*epochs
......@@ -131,6 +94,7 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
game_performance_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)]
n_react_time_per_episode = [[0 for i in range(User_React_time.counter.value)] for j in range(epochs)]
for e in range(epochs):
'''Simulation framework'''
......@@ -142,17 +106,25 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
wrong_move_counter = 0
timeout_counter = 0
max_attempt_counter = 0
selected_robot_assistance_action = 0
selected_robot_feedback_action = 0
user_dynamic_variables = {'attempt': attempt_counter_per_action,
#The following variables are used to update the BN at the end of the episode
user_action_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}
user_react_time_dynamic_variables = {'attempt': attempt_counter_per_react_time,
'game_state': game_state_counter_per_react_time,
'robot_assistance': robot_assistance_per_react_time,
'robot_feedback': robot_feedback_per_react_time}
robot_assistance_dynamic_variables = {'attempt': attempt_counter_per_robot_assistance,
'game_state': game_state_counter_per_robot_assistance}
robot_feedback_dynamic_variables = {'attempt': attempt_counter_per_robot_feedback,
'game_state': game_state_counter_per_robot_feedback}
#####################SIMULATE ONE EPISODE#########################################
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:
......@@ -162,29 +134,35 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
else:
game_state_counter = 2
robot_vars_evidence = {user_reactivity_name: user_reactivity_value,
##################QUERY FOR THE ROBOT ASSISTANCE AND FEEDBACK##################
vars_robot_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_assistance_prob = bn_functions.infer_prob_from_state(robot_bn_model,
infer_variable=var_robot_assistance_target_action,
evidence_variables=vars_robot_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)
infer_variable=var_robot_feedback_target_action,
evidence_variables=vars_robot_evidence)
selected_robot_assistance_action = bn_functions.get_stochastic_action(query_robot_ass_prob)
selected_robot_assistance_action = bn_functions.get_stochastic_action(query_robot_assistance_prob)
selected_robot_feedback_action = bn_functions.get_stochastic_action(query_robot_feedback_prob)
#counters for plots
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))
##########################QUERY FOR THE USER ACTION AND REACT TIME#####################################
#compare the real user with the estimated Persona and returns a user action (0, 1, 2)
if other_user_bn_model!=None:
#return the user action in this state based on the user profile
other_user_vars_evidence = {other_user_attention_name:other_user_attention_value,
vars_other_user_evidence = {other_user_attention_name:other_user_attention_value,
other_user_reactivity_name:other_user_reactivity_value,
other_user_memory_name:other_user_memory_value,
task_progress_name:game_state_counter,
......@@ -192,45 +170,52 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
robot_assistance_name:selected_robot_assistance_action,
robot_feedback_name:selected_robot_feedback_action
}
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)
query_user_action_prob = bn_functions.infer_prob_from_state(other_user_bn_model,
infer_variable=var_other_user_action_target_action,
evidence_variables=vars_other_user_evidence)
query_user_react_time_prob = bn_functions.infer_prob_from_state(other_user_bn_model,
infer_variable=var_other_user_target_react_time_action,
evidence_variables=vars_other_user_evidence)
else:
#return the user action in this state based on the Persona profile
user_vars_evidence = {other_user_attention_name: user_attention_value,
user_reactivity_name: user_reactivity_value,
user_memory_name: user_memory_value,
task_progress_name: game_state_counter,
game_attempt_name: attempt_counter,
robot_assistance_name: selected_robot_assistance_action,
robot_feedback_name: selected_robot_feedback_action
}
vars_user_evidence = {user_attention_name: user_attention_value,
user_reactivity_name: user_reactivity_value,
user_memory_name: user_memory_value,
task_progress_name: game_state_counter,
game_attempt_name: attempt_counter,
robot_assistance_name: selected_robot_assistance_action,
robot_feedback_name: selected_robot_feedback_action
}
query_user_action_prob = bn_functions.infer_prob_from_state(user_bn_model,
infer_variable=user_var_target_action,
evidence_variables=user_vars_evidence)
infer_variable=var_user_action_target_action,
evidence_variables=vars_user_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)
infer_variable=var_user_react_time_target_action,
evidence_variables=vars_user_evidence)
#this is needed because we are querying the system with user_react_time and user_action output is 3x3
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)
# counters for plots
n_react_time_per_episode[selected_user_react_time][e] += 1
#updates counters for user and robot model
#updates counters for user action
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
#update counter for user react time
robot_assistance_per_react_time[selected_user_react_time][selected_robot_assistance_action] += 1
attempt_counter_per_react_time[selected_user_react_time][attempt_counter] += 1
game_state_counter_per_react_time[selected_user_react_time][game_state_counter] += 1
robot_feedback_per_react_time[selected_user_react_time][selected_robot_feedback_action] += 1
#update counter for robot feedback
game_state_counter_per_robot_feedback[selected_robot_feedback_action][game_state_counter] += 1
attempt_counter_per_robot_feedback[selected_robot_feedback_action][attempt_counter] += 1
#update counter for robot assistance
game_state_counter_per_robot_assistance[selected_robot_assistance_action][game_state_counter] += 1
attempt_counter_per_robot_assistance[selected_robot_assistance_action][attempt_counter] += 1
#updates counters for simulation
iter_counter += 1
......@@ -247,12 +232,13 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
print("game_state_counter_per_action {}".format(game_state_counter_per_action))
print("robot_feedback_per_action {}".format(robot_feedback_per_action))
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)
#update user models
bn_model_user_action = bn_functions.update_cpds_tables(bn_model_user_action, user_action_dynamic_variables)
bn_model_user_react_time = bn_functions.update_cpds_tables(bn_model_user_react_time, user_react_time_dynamic_variables)
#update robot models
bn_model_robot_assistance = bn_functions.update_cpds_tables(bn_model_robot_assistance, robot_assistance_dynamic_variables)
bn_model_robot_feedback = bn_functions.update_cpds_tables(bn_model_robot_feedback, robot_feedback_dynamic_variables)
#reset counter
robot_assistance_per_action = [[0 for i in range(Robot_Assistance.counter.value)] for j in
......@@ -264,6 +250,25 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
attempt_counter_per_action = [[0 for i in range(Attempt.counter.value)] for j in
range(User_Action.counter.value)]
attempt_counter_per_react_time = [[0 for i in range(Attempt.counter.value)] for j in
range(User_React_time.counter.value)]
game_state_counter_per_react_time = [[0 for i in range(Game_State.counter.value)] for j in
range(User_React_time.counter.value)]
robot_feedback_per_react_time = [[0 for i in range(Robot_Feedback.counter.value)] for j in
range(User_React_time.counter.value)]
robot_assistance_per_react_time = [[0 for i in range(Robot_Assistance.counter.value)] for j in
range(User_React_time.counter.value)]
game_state_counter_per_robot_assistance = [[0 for i in range(Game_State.counter.value)] for j in
range(Robot_Assistance.counter.value)]
attempt_counter_per_robot_assistance = [[0 for i in range(Attempt.counter.value)] for j in
range(Robot_Assistance.counter.value)]
game_state_counter_per_robot_feedback = [[0 for i in range(Game_State.counter.value)] for j in
range(Robot_Feedback.counter.value)]
attempt_counter_per_robot_feedback = [[0 for i in range(Attempt.counter.value)] for j in
range(Robot_Feedback.counter.value)]
#for plots
n_correct_per_episode[e] = correct_move_counter
n_wrong_per_episode[e] = wrong_move_counter
......@@ -275,7 +280,7 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
n_max_attempt_per_episode[e]]
return game_performance_episode, n_assistance_lev_per_episode, n_feedback_per_episode
return game_performance_episode, n_react_time_per_episode, n_assistance_lev_per_episode, n_feedback_per_episode
......@@ -285,39 +290,38 @@ def simulation(user_bn_model, user_var_target_action, user_var_target_react_time
#############################################################################
#############################################################################
#SIMULATION PARAMS
robot_assistance = [i for i in range(Robot_Assistance.counter.value)]
robot_feedback = [i for i in range(Robot_Feedback.counter.value)]
epochs = 40
#initialise the robot
robot_cpds = bnlearn.import_DAG('bn_robot_model/robot_model.bif')
robot_assistance_bn_cpds = bnlearn.import_DAG('bn_robot_model/robot_assistive_model.bif')
robot_feedback_bn_cpds = bnlearn.import_DAG('bn_robot_model/robot_feedback_model.bif')
persona_action_bn_cpds = bnlearn.import_DAG('bn_persona_model/user_action_model.bif')
persona_react_time_bn_cpds = bnlearn.import_DAG('bn_persona_model/user_react_time_model.bif')
#initialise memory, attention and reactivity varibles
persona_memory = 0; persona_attention = 0; persona_reactivity = 1;
persona_cpds = bnlearn.import_DAG('bn_persona_model/new_persona_model.bif')
#initialise memory, attention and reactivity varibles
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_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",
robot_bn_model=robot_cpds, robot_var_ass_action=["robot_assistance"],
robot_var_feedback_action=["robot_feedback"],
game_performance_per_episode, react_time_per_episode, robot_assistance_per_episode = \
simulation(bn_model_user_action=persona_action_bn_cpds, user_var_target_action=['user_action'],
bn_model_user_react_time=persona_react_time_bn_cpds, 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",
bn_model_robot_assistance=robot_assistance_bn_cpds, robot_var_ass_action=["robot_assistance"],
bn_model_robot_feedback=robot_feedback_bn_cpds, 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,
epochs=epochs, task_complexity=5)
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, epochs=epochs, task_complexity=5)
plot_game_performance_path = ""
plot_robot_assistance_path = ""
......
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