Working code with persona, real_user and robot in the simulation

persona_model.bif → bn_persona/persona_model.bif

-network persona_model_4 {
+network persona_model {
 }
 
 %VARIABLES DEFINITION

bn_persona/utilities.py

+import random
+import bn_functions
+
+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():
+    
+
+
+
+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

persona_model_3.bif → examples/persona_model_3.bif

 network persona_model_3 {
 }
 
-%definition of the variables
+%VARIABLES DEFINITION
 variable reactivity {
   type discrete [3] {slow, medium, fast};
 }
@@ -11,29 +11,23 @@ variable memory {
 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 };
 }
 
-%definition of individual probabilities
-
+%INDIVIDUAL PROBABILITIES DEFINITION
 probability ( robot_assistance ) {
   table 0.2, 0.2, 0.2, 0.2, 0.2;
 }
@@ -46,26 +40,23 @@ probability ( attempt ) {
 probability ( user_action ) {
   table 0.33, 0.33, 0.34;
 }
-#cpds 4
+#CPDS 4
 probability ( reactivity ) {
   table 0.33, 0.33, 0.34;
 }
-#cpds 3
+#CPDS 3
 probability ( memory ) {
   table 0.33, 0.33, 0.34;
 }
-#cpds 1
+#CPDS 1
 probability ( attention ) {
   table 0.33, 0.33, 0.34;
 }
 probability ( robot_feedback ) {
   table 0.5, 0.5;
 }
-
-
-#cpds 7
+#CPDS 7
 probability (user_action | reactivity, memory, attention) {
-
 (slow, low, low)  0.1, 0.4, 0.5;
 (slow, low, medium) 0.3, 0.5, 0.2;
 (slow, low, high) 0.4, 0.5, 0.1;
@@ -75,7 +66,7 @@ probability (user_action | reactivity, memory, attention) {
 (slow, high, low)  0.3, 0.4, 0.3;
 (slow, high, medium) 0.6, 0.3, 0.1;
 (slow, high, high) 0.7, 0.2, 0.1;
-
+%%%
 (medium, low, low)  0.3, 0.4, 0.3;
 (medium, low, medium) 0.3, 0.5, 0.2;
 (medium, low, high) 0.4, 0.3, 0.3;
@@ -85,7 +76,7 @@ probability (user_action | reactivity, memory, attention) {
 (medium, high, low)  0.34, 0.33, 0.33;
 (medium, high, medium) 0.7, 0.2, 0.1;
 (medium, high, high) 0.75, 0.25, 0.0;
-
+%%%
 (fast, low, low)  0.5, 0.2, 0.3;
 (fast, low, medium) 0.6, 0.2, 0.2;
 (fast, low, high) 0.7, 0.3, 0.0;
@@ -95,37 +86,32 @@ probability (user_action | reactivity, memory, attention) {
 (fast, high, low)  0.5, 0.2, 0.3;
 (fast, high, medium) 0.6, 0.2, 0.2;
 (fast, high, high) 0.9, 0.1, 0.0;
-
 }
-
-#cpds 5
+#CPDS 5
 probability (robot_feedback | user_action) {
   (correct) 0.8, 0.2;
   (wrong) 0.5, 0.5;
   (timeout) 0.2, 0.8;
 }
-
-#cpds 6
+#CPDS 6
 probability (robot_assistance | user_action) {
   (correct) 0.05 0.1 0.15 0.3 0.4;
   (wrong) 0.1 0.2 0.4 0.2 0.1;
   (timeout) 0.2 0.4 0.2 0.1 0.1;
 }
-
-#cpds 2
+#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
+#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;