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Commit a47a5b02 authored by Antonio Andriella's avatar Antonio Andriella
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working version for experiment of the simulator

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.gitignore
+ 4
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.DS_Store
.idea/
__pycache__/
old_models/
results/
bn_agent_model/agent_feedback_model.bif deleted 100644 → 0
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network agent_feedback_model {
}
%VARIABLES DEFINITION
variable reactivity {
type discrete [3] {slow, medium, fast};
}
variable memory {
type discrete[3] {low, medium, high};
}
variable agent_feedback {
type discrete [ 2 ] { no, yes };
}
variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
variable user_capability {
type discrete [ 3 ] { very_mild, mild, severe };
}
%INDIVIDUAL PROBABILITIES DEFINITION
probability ( agent_feedback ) {
table 0.5, 0.5;
}
probability ( game_state ) {
table 0.34, 0.33, 0.33;
}
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability ( user_capability ) {
table 0.33, 0.33, 0.34;
}
#CPDS 4 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( reactivity ) {
table 0.34, 0.33, 0.33;
}
#CPDS 3 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( memory ) {
table 0.33, 0.33, 0.34;
}
#Conditional Probabilities
#CPDS X (very_mild, mild, severe)
probability (user_capability | memory, reactivity) {
(low, slow) 0.1, 0.2, 0.7;
(medium, slow) 0.2, 0.6, 0.2;
(high, slow) 0.7, 0.2, 0.1;
(low, medium) 0.1, 0.3, 0.6;
(medium, medium) 0.3, 0.6, 0.1;
(high, medium) 0.1, 0.4, 0.5;
(low, fast) 0.3, 0.2, 0.5;
(medium, fast) 0.7, 0.2, 0.1;
(high, fast) 0.8, 0.1, 0.1;
}
probability (agent_feedback | user_capability) {
(very_mild) 0.2, 0.8;
(mild) 0.5, 0.5;
(severe) 0.8, 0.2;
}
probability (game_state | agent_feedback) {
(no) 0.2, 0.4, 0.4;
(yes) 0.4, 0.4, 0.2;
}
probability (attempt | agent_feedback) {
(no) 0.1,0.15, 0.25, 0.5;
(yes) 0.4, 0.3, 0.2, 0.1;
}
bn_agent_model/utilities.py deleted 100644 → 0
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− 78
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import random
import bn_functions
def get_dynamic_variables(evidence_variables_name, evidence_variables_value):
'''
This func returns a dict of the form name:value and it defines the "evidences"
that will be used to query the BN
Args:
:evidence_variables_name: the name of the variable
:evidence_variables_value: the value of the given variable
Return:
a dict of the form name:value
'''
if len(evidence_variables_name)!=len(evidence_variables_value):
assert "The variables name numbers is different from the variables value"
else:
dynamic_variables = {evidence_variables_name[i]:evidence_variables_value[i] for i in range(len(evidence_variables_name))}
return dynamic_variables
def infer_prob(user_bn_model, variable_to_infer, evidence_vars_name, evidence_vars_value):
'''
Given the model, the variable to infer, and the evidences returns the distribution prob for that variable
Args:
user_bn_model:
variable_to_infer:
evidence_vars_name:
evidence_vars_value:
Returns:
the probability distribution for varibale_to_infer
'''
evidence = get_dynamic_variables(evidence_vars_name, evidence_vars_value)
dist_prob = bn_functions.get_inference_from_state(user_bn_model,
variables=variable_to_infer,
evidence=evidence)
return dist_prob
def get_stochastic_action(actions_distr_prob):
'''
Select one of the actions according to the actions_prob
Args:
actions_prob: the probability of the Persona based on the BN to make a correct move, wrong move, timeout
Return:
the id of the selected action
N.B:
'''
def compute_distance(values, target):
'''
Return the index of the most closest value in values to target
Args:
target: the target value
values: a list of values from 0 to 1
Return:
return the index of the value closer to target
'''
min_dist = 1
index = 0
for i in range(len(values)):
if abs(target-values[i])<min_dist:
min_dist = abs(target-values[i])
index = i
return index
actions_distr_prob_scaled = [0]*len(actions_distr_prob)
accum = 0
for i in range(len(actions_distr_prob)):
accum += actions_distr_prob[i]
actions_distr_prob_scaled[i] = accum
rnd_val = random.uniform(0, 1)
action_id = compute_distance(actions_distr_prob_scaled, rnd_val)
return action_id
actions_prob_distr = [0.32, 0.105, 0.035, 0.035, 0.005, 0.36, 0.065, 0.035, 0.035, 0.005]
action_index = get_stochastic_action(actions_prob_distr)
print(action_index)
\ No newline at end of file
bn_functions.py
+ 1
− 1
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......@@ -178,7 +178,7 @@ def get_stochastic_action(actions_distr_prob):
accum += actions_distr_prob[i]
actions_distr_prob_scaled[i] = accum
rnd_val = random.uniform(0, 1)
rnd_val = random.random()
action_id = compute_distance(actions_distr_prob_scaled, rnd_val)
return action_id
......
bn_models/agent_model_id_1_True.bif 0 → 100644
+ 40
− 0
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network agent_assistive_model {
}
%VARIABLES DEFINITION
variable agent_assistance {
type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5};
}
variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
%INDIVIDUAL PROBABILITIES DEFINITION
probability ( agent_assistance ) {
table 0.17, 0.17, 0.17, 0.17, 0.16, 0.16;
}
probability ( game_state ) {
table 0.34, 0.33, 0.33;
}
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability (agent_assistance | game_state, attempt) {
(beg, att_1) 0.2857142857142857, 0.35714285714285715, 0.14285714285714285, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142;
(beg, att_2) 0.07142857142857142, 0.21428571428571427, 0.35714285714285715, 0.21428571428571427, 0.07142857142857142, 0.07142857142857142;
(beg, att_3) 0.06666666666666667, 0.13333333333333333, 0.13333333333333333, 0.3333333333333333, 0.26666666666666666, 0.06666666666666667;
(beg, att_4) 0.058823529411764705, 0.058823529411764705, 0.11764705882352941, 0.29411764705882354, 0.23529411764705882, 0.23529411764705882;
(mid, att_1) 0.2857142857142857, 0.35714285714285715, 0.14285714285714285, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142;
(mid, att_2) 0.07142857142857142, 0.21428571428571427, 0.35714285714285715, 0.21428571428571427, 0.07142857142857142, 0.07142857142857142;
(mid, att_3) 0.06666666666666667, 0.13333333333333333, 0.13333333333333333, 0.3333333333333333, 0.26666666666666666, 0.06666666666666667;
(mid, att_4) 0.058823529411764705, 0.058823529411764705, 0.11764705882352941, 0.29411764705882354, 0.23529411764705882, 0.23529411764705882;
(end, att_1) 0.2857142857142857, 0.35714285714285715, 0.14285714285714285, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142;
(end, att_2) 0.07142857142857142, 0.21428571428571427, 0.35714285714285715, 0.21428571428571427, 0.07142857142857142, 0.07142857142857142;
(end, att_3) 0.06666666666666667, 0.13333333333333333, 0.13333333333333333, 0.3333333333333333, 0.26666666666666666, 0.06666666666666667;
(end, att_4) 0.058823529411764705, 0.058823529411764705, 0.11764705882352941, 0.29411764705882354, 0.23529411764705882, 0.23529411764705882;
}
\ No newline at end of file
bn_agent_model/agent_assistive_model.bif → bn_models/agent_model_template.bif
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......@@ -24,26 +24,3 @@ probability ( game_state ) {
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
#Conditional Probabilities
probability (agent_assistance | game_state, attempt) {
(beg, att_1) 0.5, 0.3, 0.2, 0.0, 0.0, 0.0;
(beg, att_2) 0.2, 0.3, 0.2, 0.1, 0.1, 0.1;
(beg, att_3) 0.3, 0.2, 0.2, 0.1, 0.1, 0.1;
(beg, att_4) 0.3, 0.3, 0.1, 0.1, 0.1, 0.1;
(mid, att_1) 0.5, 0.3, 0.2, 0.0, 0.0, 0.0;
(mid, att_2) 0.2, 0.3, 0.2, 0.1, 0.1, 0.1;
(mid, att_3) 0.3, 0.2, 0.2, 0.1, 0.1, 0.1;
(mid, att_4) 0.3, 0.3, 0.1, 0.1, 0.1, 0.1;
(end, att_1) 0.5, 0.3, 0.2, 0.0, 0.0, 0.0;
(end, att_2) 0.2, 0.3, 0.2, 0.1, 0.1, 0.1;
(end, att_3) 0.3, 0.2, 0.2, 0.1, 0.1, 0.1;
(end, att_4) 0.3, 0.3, 0.1, 0.1, 0.1, 0.1;
}
bn_persona_model/persona_model_test.bif → bn_models/persona_model_template.bif
+ 0
− 18
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......@@ -31,21 +31,3 @@ probability ( attempt ) {
probability ( user_action ) {
table 0.33, 0.33, 0.34;
}
probability (game_state | user_action) {
(correct) 0.30, 0.30, 0.4;
(wrong) 0.35, 0.35, 0.3;
(timeout) 0.33, 0.33, 0.34;
}
probability (attempt | user_action) {
(correct) 0.25, 0.25, 0.25, 0.25;
(wrong) 0.4, 0.3, 0.2, 0.1;
(timeout) 0.4, 0.3, 0.2, 0.1;
}
probability (user_action | agent_assistance) {
(lev_0) 0.05 0.85 0.1;
(lev_1) 0.1 0.8 0.1;
(lev_2) 0.2 0.7 0.1;
(lev_3) 0.33 0.57 0.1;
(lev_4) 0.9 0.1 0.0;
(lev_5) 1.0 0.0 0.0;
}
bn_models/user_model_id_1_True.bif 0 → 100644
+ 51
− 0
View file @ a47a5b02
network persona_model {
}
%VARIABLES DEFINITION
variable agent_assistance {
type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5 };
}
variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
variable user_action {
type discrete [ 3 ] { correct, wrong, timeout };
}
%INDIVIDUAL PROBABILITIES DEFINITION
probability ( agent_assistance ) {
table 0.17, 0.16, 0.16, 0.17, 0.17, 0.17;
}
probability ( game_state) {
table 0.34, 0.33, 0.33;
}
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability ( user_action ) {
table 0.33, 0.33, 0.34;
}
probability (game_state | user_action) {
(correct) 0.2222222222222222,0.3333333333333333,0.4444444444444444;
(wrong) 0.5,0.3333333333333333,0.16666666666666666;
(timeout) 0.5,0.3333333333333333,0.16666666666666666;
}
probability (attempt | user_action) {
(correct) 0.15384615384615385,0.23076923076923078,0.3076923076923077,0.3076923076923077;
(wrong) 0.42857142857142855,0.2857142857142857,0.14285714285714285,0.14285714285714285;
(timeout) 0.42857142857142855,0.2857142857142857,0.14285714285714285,0.14285714285714285;
}
probability (user_action | agent_assistance) {
(lev_0) 0.4,0.3,0.3;
(lev_1) 0.6,0.2,0.2;
(lev_2) 0.6,0.2,0.2;
(lev_3) 0.8,0.1,0.1;
(lev_4) 1.0,0.0,0.0;
(lev_5) 1.0,0.0,0.0;
}
\ No newline at end of file
bn_other_user_model/user_model.bif deleted 100644 → 0
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network persona_model_4 {
}
%VARIABLES DEFINITION
variable reactivity {
type discrete [3] {slow, medium, fast};
}
variable memory {
type discrete[3] {low, medium, high};
}
variable attention {
type discrete[3] {low, medium, high};
}
variable robot_assistance {
type discrete [ 5 ] { lev_0, lev_1, lev_2, lev_3, lev_4 };
}
variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
variable robot_feedback {
type discrete [ 2 ] { yes, no };
}
variable user_action {
type discrete [ 3 ] { correct, wrong, timeout };
}
%INDIVIDUAL PROBABILITIES DEFINITION
probability ( robot_assistance ) {
table 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( game_state ) {
table 0.34, 0.33, 0.33;
}
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability ( user_action ) {
table 0.33, 0.33, 0.34;
}
#CPDS 4 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( reactivity ) {
table 0.34, 0.33, 0.33;
}
#CPDS 3 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( memory ) {
table 0.33, 0.33, 0.34;
}
#CPDS 1 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( attention ) {
table 0.33, 0.33, 0.34;
}
probability ( robot_feedback ) {
table 0.5, 0.5;
}
probability ( reactivity | attention ) {
(low) 0.5, 0.4, 0.1;
(medium) 0.3, 0.5, 0.2;
(high) 0.1, 0.2, 0.7;
}
#CPDS 7
probability (user_action | memory, reactivity) {
(low, slow) 0.2, 0.5, 0.3;
(low, medium) 0.3, 0.5, 0.2;
(low, fast) 0.4, 0.5, 0.1;
(medium, slow) 0.5, 0.3, 0.2;
(medium, medium) 0.55, 0.35, 0.1;
(medium, fast) 0.6, 0.4, 0.0;
(high, slow) 0.5, 0.4, 0.1;
(high, medium) 0.6, 0.3, 0.1;
(high, fast) 0.8, 0.2, 0.0;
}
#CPDS 5
probability (robot_feedback | user_action) {
(correct) 0.5, 0.5;
(wrong) 0.5, 0.5;
(timeout) 0.5, 0.5;
}
#CPDS 6
probability (robot_assistance | user_action) {
(correct) 0.05 0.1 0.15 0.3 0.4;
(wrong) 0.4 0.2 0.2 0.1 0.1;
(timeout) 0.4 0.2 0.2 0.1 0.1;
}
#CPDS 2
probability (game_state | user_action) {
(correct) 0.2, 0.4, 0.4;
(wrong) 0.4, 0.4, 0.2;
(timeout) 0.6, 0.3, 0.1;
}
#CPDS 0
probability (attempt | user_action) {
(correct) 0.1, 0.2, 0.3, 0.4;
(wrong) 0.7, 0.2, 0.1, 0.0;
(timeout) 0.6, 0.3, 0.1, 0.0;
}
#CPDS 5
probability (robot_assistance | robot_feedback) {
(yes) 0.5 0.3 0.1 0.1 0.0;
(no) 0.0 0.1 0.1 0.3 0.5;
}
\ No newline at end of file
bn_persona_model/__pycache__/utilities.cpython-36.pyc deleted 100644 → 0
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File deleted
bn_persona_model/persona_model.bif deleted 100644 → 0
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network persona_model {
}
%VARIABLES DEFINITION
variable reactivity {
type discrete [3] {slow, medium, fast};
}
variable memory {
type discrete[3] {low, medium, high};
}
variable attention {
type discrete[3] {low, medium, high};
}
variable robot_assistance {
type discrete [ 5 ] { lev_0, lev_1, lev_2, lev_3, lev_4 };
}
variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
variable robot_feedback {
type discrete [ 2 ] { yes, no };
}
variable user_action {
type discrete [ 3 ] { correct, wrong, timeout };
}
%INDIVIDUAL PROBABILITIES DEFINITION
probability ( robot_assistance ) {
table 0.2, 0.2, 0.2, 0.2, 0.2;
}
probability ( game_state ) {
table 0.34, 0.33, 0.33;
}
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability ( user_action ) {
table 0.33, 0.33, 0.34;
}
#CPDS 4 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( reactivity ) {
table 0.34, 0.33, 0.33;
}
#CPDS 3 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( memory ) {
table 0.33, 0.33, 0.34;
}
#CPDS 1 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( attention ) {
table 0.33, 0.33, 0.34;
}
probability ( robot_feedback ) {
table 0.5, 0.5;
}
probability ( reactivity | attention ) {
(low) 0.7, 0.2, 0.1;
(medium) 0.2, 0.5, 0.3;
(high) 0.1, 0.2, 0.7;
}
#CPDS 7
probability (user_action | memory, reactivity) {
(low, slow) 0.2, 0.5, 0.3;
(low, medium) 0.3, 0.5, 0.2;
(low, fast) 0.4, 0.5, 0.1;
(medium, slow) 0.5, 0.3, 0.2;
(medium, medium) 0.55, 0.35, 0.1;
(medium, fast) 0.6, 0.4, 0.0;
(high, slow) 0.5, 0.4, 0.1;
(high, medium) 0.6, 0.3, 0.1;
(high, fast) 0.8, 0.2, 0.0;
}
#CPDS 5
probability (robot_feedback | user_action) {
(correct) 0.8, 0.2;
(wrong) 0.5, 0.5;
(timeout) 0.2, 0.8;
}
#CPDS 6
probability (robot_assistance | user_action) {
(correct) 0.05 0.1 0.15 0.3 0.4;
(wrong) 0.4 0.3 0.2 0.05 0.05;
(timeout) 0.4 0.4 0.1 0.05 0.05;
}
#CPDS 2
probability (game_state | user_action) {
(correct) 0.2, 0.4, 0.4;
(wrong) 0.4, 0.4, 0.2;
(timeout) 0.6, 0.3, 0.1;
}
#CPDS 0
probability (attempt | user_action) {
(correct) 0.1, 0.2, 0.3, 0.4;
(wrong) 0.5, 0.3, 0.15, 0.05;
(timeout) 0.4, 0.3, 0.2, 0.1;
}
#CPDS 5
probability (robot_assistance | robot_feedback) {
(yes) 0.5 0.3 0.1 0.1 0.0;
(no) 0.0 0.1 0.1 0.3 0.5;
}
\ No newline at end of file
bn_persona_model/user_action_model.bif deleted 100644 → 0
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− 84
View file @ 68dca4fa
network persona_model {
}
%VARIABLES DEFINITION
variable user_reactivity {
type discrete [3] {slow, medium, fast};
}
variable user_memory {
type discrete[3] {low, medium, high};
}
variable agent_assistance {
type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5 };
}
variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
variable agent_feedback {
type discrete [ 2 ] { yes, no };
}
variable user_action {
type discrete [ 3 ] { correct, wrong, timeout };
}
%INDIVIDUAL PROBABILITIES DEFINITION
probability ( agent_assistance ) {
table 0.16, 0.16, 0.17, 0.16, 0.17, 0.16;
}
probability ( game_state ) {
table 0.34, 0.33, 0.33;
}
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
probability ( user_action ) {
table 0.33, 0.33, 0.34;
}
#CPDS 4 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( user_reactivity ) {
table 0.34, 0.33, 0.33;
}
#CPDS 3 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( user_memory ) {
table 0.33, 0.33, 0.34;
}
probability ( agent_feedback ) {
table 0.5, 0.5;
}
probability (user_action | user_memory, user_reactivity) {
(low, slow) 0.2, 0.2, 0.6;
(low, medium) 0.3, 0.2, 0.5;
(low, fast) 0.4, 0.4, 0.2;
(medium, slow) 0.3, 0.1, 0.6;
(medium, medium) 0.4, 0.2, 0.4;
(medium, fast) 0.6, 0.2, 0.2;
(high, slow) 0.7, 0.1, 0.2;
(high, medium) 0.8, 0.1, 0.1;
(high, fast) 0.8, 0.2, 0.0;
}
probability (agent_feedback | user_action) {
(correct) 0.8, 0.2;
(wrong) 0.5, 0.5;
(timeout) 0.2, 0.8;
}
probability (agent_assistance | user_action) {
(correct) 0.05 0.05 0.1 0.1 0.35 0.35;
(wrong) 0.45 0.3 0.2 0.05 0.0 0.0;
(timeout) 0.4 0.4 0.2 0.0 0.0 0.0;
}
probability (game_state | user_action) {
(correct) 0.2, 0.4, 0.4;
(wrong) 0.4, 0.4, 0.2;
(timeout) 0.6, 0.3, 0.1;
}
probability (attempt | user_action) {
(correct) 0.1, 0.2, 0.3, 0.4;
(wrong) 0.5, 0.3, 0.15, 0.05;
(timeout) 0.4, 0.3, 0.2, 0.1;
}
\ No newline at end of file
bn_persona_model/user_react_time_model.bif deleted 100644 → 0
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network persona_model {
}
%VARIABLES DEFINITION
variable reactivity {
type discrete [3] {slow, medium, fast};
}
variable memory {
type discrete[3] {low, medium, high};
}
variable attention {
type discrete[3] {low, medium, high};
}
variable agent_assistance {
type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5 };
}
variable attempt {
type discrete [ 4 ] { att_1, att_2, att_3, att_4 };
}
variable game_state {
type discrete [ 3 ] { beg, mid, end };
}
variable agent_feedback {
type discrete [ 2 ] { yes, no };
}
variable user_react_time {
type discrete [ 3 ] { slow, normal, fast};
}
%INDIVIDUAL PROBABILITIES DEFINITION
variable agent_assistance {
type discrete [ 6 ] { lev_0, lev_1, lev_2, lev_3, lev_4, lev_5 };
}
probability ( game_state ) {
table 0.34, 0.33, 0.33;
}
probability ( attempt ) {
table 0.25, 0.25, 0.25, 0.25;
}
#CPDS 4 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( reactivity ) {
table 0.34, 0.33, 0.33;
}
#CPDS 3 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( memory ) {
table 0.33, 0.33, 0.34;
}
#CPDS 1 #SPECIFICALLY FOR THE GIVEN PATIENT
probability ( attention ) {
table 0.33, 0.33, 0.34;
}
probability ( agent_feedback ) {
table 0.5, 0.5;
}
probability (user_react_time) {
table 0.33, 0.33, 0.34
}
probability ( reactivity | attention ) {
(low) 0.7, 0.2, 0.1;
(medium) 0.2, 0.5, 0.3;
(high) 0.1, 0.2, 0.7;
}
probability (user_react_time | memory, reactivity) {
(low, slow) 0.2, 0.5, 0.3;
(low, medium) 0.3, 0.5, 0.2;
(low, fast) 0.4, 0.5, 0.1;
(medium, slow) 0.5, 0.3, 0.2;
(medium, medium) 0.55, 0.35, 0.1;
(medium, fast) 0.6, 0.4, 0.0;
(high, slow) 0.5, 0.4, 0.1;
(high, medium) 0.6, 0.3, 0.1;
(high, fast) 0.8, 0.2, 0.0;
}
probability (agent_feedback | user_react_time) {
(slow) 0.8, 0.2;
(normal) 0.5, 0.5;
(fast) 0.2, 0.8;
}
probability (agent_assistance | user_react_time) {
(slow) 0.05 0.05 0.1 0.1 0.35 0.35;
(normal) 0.45 0.3 0.2 0.05 0.0 0.0;
(fast) 0.4 0.4 0.2 0.0 0.0 0.0;
}
probability (game_state | user_react_time) {
(slow) 0.2, 0.4, 0.4;
(normal) 0.4, 0.4, 0.2;
(fast) 0.6, 0.3, 0.1;
}
probability (attempt | user_react_time) {
(slow) 0.1, 0.2, 0.3, 0.4;
(normal) 0.5, 0.3, 0.15, 0.05;
(fast) 0.4, 0.3, 0.2, 0.1;
}
bn_persona_model/utilities.py deleted 100644 → 0
+ 0
− 90
View file @ 68dca4fa
import random
import bn_functions
def get_dynamic_variables(variables_name, variables_value):
if len(variables_name)!=len(variables_value):
assert "The variables name numbers is different from the variables value"
else:
dynamic_variables = {variables_name[i]:variables_value[i] for i in range(len(variables_name))}
return dynamic_variables
def compute_next_state(user_action, task_evolution, attempt_counter, correct_move_counter,
wrong_move_counter, timeout_counter
):
'''
The function computes given the current state and action of the user, the next state
Args:
user_action: 0,1,2
task_evolution: beg, mid, end
correct_move_counter:
attempt_counter:
wrong_move_counter:
timeout_counter:
Return:
the counters updated according to the user_action
'''
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
timeout_counter += 1
# the robot or therapist makes the correct move on the patient's behalf
else:
attempt_counter = 0
task_evolution += 1
correct_move_counter += 1
return task_evolution, attempt_counter, correct_move_counter, wrong_move_counter, timeout_counter
def get_user_action_prob(user_bn_model, robot_assistance_action, robot_feedback_action,
attempt_counter, game_state_counter, user_memory, user_attention, user_reactivity):
user_actions_prob = bn_functions.get_inference_from_state(user_bn_model,
variables=['user_action'],
evidence={'robot_assistance': robot_assistance_action,
'attempt': attempt_counter,
'game_state': game_state_counter,
'robot_feedback': robot_feedback_action,
'memory': user_memory,
'attention': user_attention,
'reactivity': user_reactivity})
return user_actions_prob
def get_stochatic_action(actions_prob):
'''
Select one of the actions according to the actions_prob
Args:
actions_prob: the probability of the Persona based on the BN to make a correct move, wrong move, timeout
Return:
the id of the selected action
N.B:
'''
action_id = None
correct_action_from_BN = actions_prob[0]
wrong_action_from_BN = actions_prob[1]
timeout_action_from_BN = actions_prob[2]
rnd_val = random.uniform(0,1)
#if user_prob is lower than the correct action prob then is the correct one
if rnd_val<=correct_action_from_BN:
action_id = 0
#if rnd is larger than the correct action prob and lower than wrong
# action prob then is the wrong one
elif rnd_val>correct_action_from_BN \
and rnd_val<(correct_action_from_BN+wrong_action_from_BN):
action_id = 1
#timeout
else:
action_id = 2
return action_id
bn_robot_model/__pycache__/utilities.cpython-36.pyc deleted 100644 → 0
+ 0
− 0
View file @ 68dca4fa
File deleted
simulation.py
+ 165
− 165
View file @ a47a5b02
......@@ -3,6 +3,7 @@ import os
import bnlearn
import numpy as np
import random
import copy
#import classes and modules
from bn_variables import Agent_Assistance, Agent_Feedback, User_Action, User_React_time, Game_State, Attempt
import bn_functions
......@@ -156,10 +157,6 @@ def compute_next_state(user_action, task_progress_counter, attempt_counter, corr
task_progress_counter +=1
print("Reach the end of the episode")
# TODO call the function to compute the state of the game (beg, mid, end)
next_state = (game_state_counter, attempt_counter, user_action)
......@@ -177,20 +174,19 @@ def select_agent_action(agent_action, epsilon):
if random.random()>epsilon:
return np.argmax(agent_action)
else:
agent_action[np.argmax(agent_action)]=0
return np.argmax(agent_action)
agent_action_rm_best = agent_action[:]
agent_action_rm_best[np.argmax(agent_action)] = 0
return np.argmax(agent_action_rm_best)
def simulation(bn_model_user_action,
bn_model_agent_behaviour,
var_user_action_target_action,
var_agent_behaviour_target_action,
game_state_bn_name, attempt_bn_name,
agent_assistance_bn_name, agent_feedback_bn_name,
user_pref_assistance,
agent_behaviour,
agent_assistance_bn_name,
agent_policy,
state_space, action_space,
epochs=50, task_complexity=5, max_attempt_per_object=4, alpha_learning=0):
epoch=50, run = 50, task_complexity=5, max_attempt_per_object=4, alpha_learning=0):
'''
Args:
......@@ -201,170 +197,174 @@ def simulation(bn_model_user_action,
'''
# user_action_per_robot_feedback_robot_assistance = [[[0 for i in range(User_Action.counter.value)]
# for j in range(Agent_Assistance.counter.value)]
# for l in range(Agent_Feedback.counter.value)
# ]
# attempt_counter_per_user_action = [[0 for i in range(Attempt.counter.value)] for j in
# range(User_Action.counter.value)]
# game_state_counter_per_user_action = [[0 for i in range(Game_State.counter.value)] for j in
# range(User_Action.counter.value)]
user_action_per_agent_assistance = [[0 for i in range(User_Action.counter.value)]
for j in range(Agent_Assistance.counter.value)]
attempt_counter_per_user_action = [[0 for i in range(Attempt.counter.value)] for j in
range(User_Action.counter.value)]
game_state_counter_per_user_action = [[0 for i in range(Game_State.counter.value)] for j in
range(User_Action.counter.value)]
#output variables:
n_correct_per_episode = [0]*epochs
n_wrong_per_episode = [0]*epochs
n_timeout_per_episode = [0]*epochs
n_max_attempt_per_episode = [0]*epochs
game_performance_episode = [0]*epochs
n_assistance_lev_per_episode = [[0 for i in range(Agent_Assistance.counter.value)] for j in range(epochs)]
n_feedback_per_episode = [[0 for i in range(Agent_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)]
n_correct_per_episode_epoch = [0]*epoch
n_wrong_per_episode_epoch = [0]*epoch
n_timeout_per_episode_epoch = [0]*epoch
n_max_attempt_per_episode_epoch = [0]*epoch
game_performance_episode_epoch = [0]*epoch
n_assistance_lev_per_episode_epoch = [[0 for i in range(Agent_Assistance.counter.value)] for j in range(epoch)]
#data structure to memorise a sequence of episode
episodes = []
ep = Episode()
bn_model_user_action_ref = copy.deepcopy(bn_model_user_action)
for e in range(epochs):
for e in range(epoch):
print("##########################################################")
print("EPISODE ",e)
print("EPISODE ", e)
print("##########################################################")
'''Simulation framework'''
#counters
game_state_counter = 0
attempt_counter = 1
iter_counter = 0
correct_move_counter = 0
wrong_move_counter = 0
timeout_counter = 0
max_attempt_counter = 0
#The following variables are used to update the BN at the end of the episode
# user_action_dynamic_variables = {
# 'attempt': attempt_counter_per_user_action,
# 'game_state': game_state_counter_per_user_action,
# 'user_action': user_action_per_robot_feedback_robot_assistance
# }
#data structure to memorise the sequence of states (state, action, next_state)
episode = []
selected_user_action = 0
task_progress_counter = 0
#####################SIMULATE ONE EPISODE#########################################
while(task_progress_counter<=task_complexity):
current_state = (game_state_counter, attempt_counter, selected_user_action)
current_state_index = ep.state_from_point_to_index(state_space, current_state)
# if agent_policy==[]:
# selected_agent_feedback_action, selected_agent_assistance_action = \
# generate_agent_assistance(preferred_assistance=user_pref_assistance,
# agent_behaviour=agent_behaviour,
# current_state=current_state_index,
# state_space=state_space,
# action_space=action_space
# )
# else:
# #TODO agent_policy is a list of 12 items
# # select the one with the highest probability 1-epsilon of the times and one of the others epsilon times
#
# selected_agent_feedback_action, selected_agent_assistance_action = ep.state_from_index_to_point(action_space, select_agent_action(agent_policy[current_state_index], epsilon=0.1))
vars_agent_evidence = {game_state_bn_name: game_state_counter,
attempt_bn_name: attempt_counter - 1,
}
query_agent_behaviour_prob = bn_functions.infer_prob_from_state(user_bn_model=bn_model_agent_behaviour,
infer_variable=var_agent_behaviour_target_action,
evidence_variables=vars_agent_evidence)
selected_agent_behaviour_action = bn_functions.get_stochastic_action(query_agent_behaviour_prob.values)
#selected_agent_behaviour_action = np.argmax(query_agent_behaviour_prob.values)
#counters for plots
n_assistance_lev_per_episode[e][selected_agent_behaviour_action] += 1
print("agent_assistance {}, attempt {}, game {}".format(selected_agent_behaviour_action, attempt_counter, game_state_counter))
##########################QUERY FOR THE USER ACTION AND REACT TIME#####################################
#return the user action in this state based on the Persona profile
vars_user_evidence = { game_state_bn_name: game_state_counter,
attempt_bn_name: attempt_counter - 1,
agent_assistance_bn_name: selected_agent_behaviour_action,
}
query_user_action_prob = bn_functions.infer_prob_from_state(user_bn_model=bn_model_user_action,
infer_variable=var_user_action_target_action,
evidence_variables=vars_user_evidence)
selected_user_action = bn_functions.get_stochastic_action(query_user_action_prob.values)
# # updates counters for simulation
# # remap user_action index
# if selected_user_action == 0:
# selected_user_action = 1
# elif selected_user_action == 1:
# selected_user_action = -1
# else:
# selected_user_action = 0
#updates counters for simulation
iter_counter += 1
next_state, task_progress_counter, game_state_counter, attempt_counter, correct_move_counter, \
wrong_move_counter, timeout_counter, max_attempt_counter = compute_next_state(selected_user_action,
task_progress_counter,
attempt_counter,
correct_move_counter, wrong_move_counter,
timeout_counter, max_attempt_counter,
max_attempt_per_object)
# update counters
# if game_state_counter <= 2:
# user_action_per_robot_feedback_robot_assistance[selected_agent_feedback_action][selected_agent_assistance_action][selected_user_action] += 1
# attempt_counter_per_user_action[selected_user_action][attempt_counter - 1] += 1
# game_state_counter_per_user_action[selected_user_action][game_state_counter] += 1
# store the (state, action, next_state)
episode.append((ep.state_from_point_to_index(state_space, current_state),
selected_agent_behaviour_action,
ep.state_from_point_to_index(state_space, next_state)))
print("current_state ", current_state, " next_state ", next_state)
####################################END of EPISODE#######################################
print("game_state_counter {}, iter_counter {}, correct_counter {}, wrong_counter {}, "
"timeout_counter {}, max_attempt {}".format(game_state_counter, iter_counter, correct_move_counter,
wrong_move_counter, timeout_counter, max_attempt_counter))
#save episode
episodes.append(Episode(episode))
#update user models
# bn_model_user_action = bn_functions.update_cpds_tables(bn_model_user_action, user_action_dynamic_variables, alpha_learning)
#
#reset counter
user_action_per_robot_feedback_robot_assistance = [[[0 for i in range(User_Action.counter.value)]
for j in range(Agent_Assistance.counter.value)]
for l in range(Agent_Feedback.counter.value)
]
attempt_counter_per_user_action = [[0 for i in range(Attempt.counter.value)] for j in
range(User_Action.counter.value)]
game_state_counter_per_user_action = [[0 for i in range(Game_State.counter.value)] for j in
range(User_Action.counter.value)]
#for plots
n_correct_per_episode[e] = correct_move_counter
n_wrong_per_episode[e] = wrong_move_counter
n_timeout_per_episode[e] = timeout_counter
n_max_attempt_per_episode[e] = max_attempt_counter
game_performance_episode[e] = [n_correct_per_episode[e],
n_wrong_per_episode[e],
n_timeout_per_episode[e],
n_max_attempt_per_episode[e]]
return game_performance_episode, n_react_time_per_episode, n_assistance_lev_per_episode, n_feedback_per_episode, episodes
bn_model_user_action = copy.deepcopy(bn_model_user_action_ref)
n_correct_per_episode_run = [0] * run
n_wrong_per_episode_run = [0] * run
n_timeout_per_episode_run = [0] * run
n_max_attempt_per_episode_run = [0] * run
game_performance_episode_run = [0] * run
n_assistance_lev_per_episode_run = [[0 for i in range(Agent_Assistance.counter.value)] for j in range(run)]
for r in range(run):
'''Simulation framework'''
#counters
game_state_counter = 0
attempt_counter = 1
iter_counter = 0
correct_move_counter = 0
wrong_move_counter = 0
timeout_counter = 0
max_attempt_counter = 0
#The following variables are used to update the BN at the end of the episode
user_action_dynamic_variables = {
'attempt': attempt_counter_per_user_action,
'game_state': game_state_counter_per_user_action,
'user_action': user_action_per_agent_assistance
}
#data structure to memorise the sequence of states (state, action, next_state)
episode = []
selected_user_action = 0
task_progress_counter = 0
#####################SIMULATE ONE EPISODE#########################################
while(task_progress_counter<=task_complexity):
current_state = (game_state_counter, attempt_counter, selected_user_action)
current_state_index = ep.state_from_point_to_index(state_space, current_state)
if agent_policy==[]:
vars_agent_evidence = {game_state_bn_name: game_state_counter,
attempt_bn_name: attempt_counter - 1,
}
query_agent_behaviour_prob = bn_functions.infer_prob_from_state(user_bn_model=bn_model_agent_behaviour,
infer_variable=var_agent_behaviour_target_action,
evidence_variables=vars_agent_evidence)
#selected_agent_behaviour_action = bn_functions.get_stochastic_action(query_agent_behaviour_prob.values)
selected_agent_behaviour_action = select_agent_action(query_agent_behaviour_prob.values, epsilon=0.2)
else:
selected_agent_behaviour_action = select_agent_action(agent_policy[current_state_index], epsilon=0.2)
#selected_agent_behaviour_action = bn_functions.get_stochastic_action(agent_policy[current_state_index])
#selected_agent_behaviour_action =np.argmax(agent_policy[current_state_index])
#counters for plots
n_assistance_lev_per_episode_run[r][selected_agent_behaviour_action] += 1
print("agent_assistance {}, attempt {}, game {}".format(selected_agent_behaviour_action, attempt_counter, game_state_counter))
##########################QUERY FOR THE USER ACTION AND REACT TIME#####################################
#return the user action in this state based on the Persona profile
vars_user_evidence = { game_state_bn_name: game_state_counter,
attempt_bn_name: attempt_counter - 1,
agent_assistance_bn_name: selected_agent_behaviour_action,
}
query_user_action_prob = bn_functions.infer_prob_from_state(user_bn_model=bn_model_user_action,
infer_variable=var_user_action_target_action,
evidence_variables=vars_user_evidence)
selected_user_action = bn_functions.get_stochastic_action(query_user_action_prob.values)
#selected_user_action = np.argmax(query_user_action_prob.values)
#updates counters for simulation
iter_counter += 1
next_state, task_progress_counter, game_state_counter, attempt_counter, correct_move_counter, \
wrong_move_counter, timeout_counter, max_attempt_counter = compute_next_state(selected_user_action,
task_progress_counter,
attempt_counter,
correct_move_counter, wrong_move_counter,
timeout_counter, max_attempt_counter,
max_attempt_per_object)
# update counters
if game_state_counter <= 2:
user_action_per_agent_assistance[selected_agent_behaviour_action][selected_user_action] += 1
attempt_counter_per_user_action[selected_user_action][attempt_counter - 1] += 1
game_state_counter_per_user_action[selected_user_action][game_state_counter] += 1
# store the (state, action, next_state)
episode.append((ep.state_from_point_to_index(state_space, current_state),
selected_agent_behaviour_action,
ep.state_from_point_to_index(state_space, next_state)))
print("current_state ", current_state, " user_action:", selected_user_action, " next_state ", next_state)
####################################END of EPISODE#######################################
print("game_state_counter {}, iter_counter {}, correct_counter {}, wrong_counter {}, "
"timeout_counter {}, max_attempt {}".format(game_state_counter, iter_counter, correct_move_counter,
wrong_move_counter, timeout_counter, max_attempt_counter))
#save episode
episodes.append(Episode(episode))
#update user models
# bn_model_user_action = bn_functions.update_cpds_tables(bn_model_user_action, user_action_dynamic_variables, alpha_learning)
#reset counter
user_action_per_agent_assistance = [[0 for i in range(User_Action.counter.value)]
for j in range(Agent_Assistance.counter.value)]
attempt_counter_per_user_action = [[0 for i in range(Attempt.counter.value)] for j in
range(User_Action.counter.value)]
game_state_counter_per_user_action = [[0 for i in range(Game_State.counter.value)] for j in
range(User_Action.counter.value)]
#for plots
n_correct_per_episode_run[r] = correct_move_counter
n_wrong_per_episode_run[r] = wrong_move_counter
n_timeout_per_episode_run[r] = timeout_counter
n_max_attempt_per_episode_run[r] = max_attempt_counter
game_performance_episode_run[r] = [n_correct_per_episode_run[r],
n_wrong_per_episode_run[r],
n_timeout_per_episode_run[r],
n_max_attempt_per_episode_run[r]]
#compute average of the values for one epoch and store it
n_correct_per_episode_epoch[e] = sum(n_correct_per_episode_run)/run
n_wrong_per_episode_epoch[e] = sum(n_wrong_per_episode_run)/run
n_timeout_per_episode_epoch[e] = sum(n_timeout_per_episode_run)/run
n_max_attempt_per_episode_epoch[e] = sum(n_max_attempt_per_episode_run)/run
game_performance_episode_epoch[e] = list(map(lambda x: sum(x)/run, zip(*game_performance_episode_run)))
n_assistance_lev_per_episode_epoch[e] = list(map(lambda x: sum(x)/run, zip(*n_assistance_lev_per_episode_run)))
#reset variables
n_correct_per_episode_run = [0] * run
n_wrong_per_episode_run = [0] * run
n_timeout_per_episode_run = [0] * run
n_max_attempt_per_episode_run = [0] * run
game_performance_episode_run = [0] * run
n_assistance_lev_per_episode_run = [[0 for i in range(Agent_Assistance.counter.value)] for j in range(run)]
return game_performance_episode_epoch, n_assistance_lev_per_episode_epoch, episodes
......@@ -380,7 +380,7 @@ def simulation(bn_model_user_action,
# epochs = 20
# scaling_factor = 1
# # initialise the agent
# bn_model_user_action = bnlearn.import_DAG('/home/pal/Documents/Framework/bn_generative_model/bn_persona_model/persona_model_test.bif')
# bn_model_user_action = bnlearn.import_DAG('/home/pal/Documents/Framework/bn_generative_model/bn_persona_model/persona_model_template.bif')
#
# # initialise memory, attention and reactivity variables
# persona_memory = 0;
......
test.py
+ 17
− 18
View file @ a47a5b02
......@@ -25,29 +25,28 @@ def import_data_from_csv(csv_filename, dag_filename):
DAG_shared = import_data_from_csv(csv_filename='bn_persona_model/cognitive_game.csv', dag_filename='bn_persona_model/persona_model_test.bif')
#DAG_shared = import_data_from_csv(csv_filename='/data/test.csv', dag_filename='bn_persona_model/persona_model_template.bif')
# DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif')
DAG_agent = bn.import_DAG('old_models/bn_agent_model/agent_test.bif')
DAG_user = bn.import_DAG('old_models/bn_persona_model/persona_test.bif')
# #G = bn.plot(DAG)
#
# q_origin = bn.inference.fit(DAG, variables=[ 'user_action'], evidence={
# q_origin = bn.inference.fit(DAG_agent, variables=[ 'agent_assistance'], evidence={
# 'game_state':0,
# 'attempt':0,
# 'agent_feedback':0,
# 'agent_assistance':0,
# 'attempt':3,
# #'agent_assistance':2,
# })
# q_shared = bn.inference.fit(DAG_shared, variables=[ 'user_action'], evidence={
# 'game_state':0,
# 'attempt':0,
# 'agent_feedback':1,
# 'user_memory': 2,
# 'user_reactivity':2,
# 'agent_assistance':0,
# })
#
# print("Q origin: ", q_origin.values, " Q shared ", q_shared.values)
q_shared = bn.inference.fit(DAG_user, variables=[ 'user_action'], evidence={
'game_state':0,
'attempt':3,
'agent_assistance':0,
})
print( " Q shared ", q_shared.values)
# df = pd.read_csv('bn_persona_model/cognitive_game.csv')
# df = bn.sampling(DAG, n=10000)
# #model_sl = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
......@@ -72,10 +71,10 @@ DAG_shared = import_data_from_csv(csv_filename='bn_persona_model/cognitive_game.
# print("GS:", gs, " ATT:", att, " AA", aas, " AF", af)
#
# df.head()
# DAG = bn.import_DAG('bn_persona_model/persona_model_test.bif', CPD=False)
# DAG = bn.import_DAG('bn_persona_model/persona_model_template.bif', CPD=False)
# bn.plot(DAG)
# DAG_update = bn.parameter_learning.fit(DAG, df)
# DAG_true = bn.import_DAG('bn_persona_model/persona_model_test.bif', CPD=True)
# DAG_true = bn.import_DAG('bn_persona_model/persona_model_template.bif', CPD=True)
# q1 = bn.inference.fit(DAG_update, variables=['user_action'], evidence={
# 'game_state': 0,
# 'attempt':2,
......
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