diff --git a/bn_models/agent_model_id_1_True.bif b/bn_models/agent_model_id_1_True.bif
deleted file mode 100644
index ebc8cee75c6472cfe11647188fe3c0aea77d50f2..0000000000000000000000000000000000000000
--- a/bn_models/agent_model_id_1_True.bif
+++ /dev/null
@@ -1,40 +0,0 @@
-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
diff --git a/bn_models/agent_model_template.bif b/bn_models/agent_model_template.bif
deleted file mode 100644
index 1ca11f0133460feef8789a72c56f5764d688d8fa..0000000000000000000000000000000000000000
--- a/bn_models/agent_model_template.bif
+++ /dev/null
@@ -1,26 +0,0 @@
-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;
-}
diff --git a/bn_models/persona_model_template.bif b/bn_models/persona_model_template.bif
deleted file mode 100644
index d8227eaa35963ecfe91423854c19ea8ac6fdcafb..0000000000000000000000000000000000000000
--- a/bn_models/persona_model_template.bif
+++ /dev/null
@@ -1,33 +0,0 @@
-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;
-}
diff --git a/bn_models/user_model_id_1_True.bif b/bn_models/user_model_id_1_True.bif
deleted file mode 100644
index 3f1ae72adce9568df8bf01f4df5d1f28b9b1571d..0000000000000000000000000000000000000000
--- a/bn_models/user_model_id_1_True.bif
+++ /dev/null
@@ -1,51 +0,0 @@
-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
diff --git a/simulation.py b/simulation.py
index 15bd225bb57ed75937340104501069a88f760768..ab3029f995a6c9942e0928788b6562a8a4167366 100644
--- a/simulation.py
+++ b/simulation.py
@@ -163,20 +163,37 @@ def compute_next_state(user_action, task_progress_counter, attempt_counter, corr
return next_state, task_progress_counter, game_state_counter, attempt_counter, correct_move_counter, wrong_move_counter, timeout_counter, max_attempt_counter
-def select_agent_action(agent_action, epsilon):
+def select_agent_action(agent_action, agent_objective, epsilon):
'''
Args:
agent_action: list of possible actions with their probabilities
+ epsilon: probability at which the agent selects a non optimal action
+ objective: it can be, None, challenge, and help
Return:
one of the agent's actions
'''
- if random.random()>epsilon:
- return np.argmax(agent_action)
+ if agent_objective == "help":
+ if random.random() < epsilon:
+ return np.argmax(agent_action)
+ else:
+ agent_best_action = np.argmax(agent_action)
+ agent_help_action = min(5, agent_best_action+1)
+ return agent_help_action
+ elif agent_objective == "challenge":
+ if random.random() < epsilon:
+ return np.argmax(agent_action)
+ else:
+ agent_best_action = np.argmax(agent_action)
+ agent_challenge_action = max(0, agent_best_action-1)
+ return agent_challenge_action
else:
- agent_action_rm_best = agent_action[:]
- agent_action_rm_best[np.argmax(agent_action)] = 0
- return np.argmax(agent_action_rm_best)
+ if random.random() > epsilon:
+ return np.argmax(agent_action)
+ else:
+ 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,
@@ -185,8 +202,10 @@ def simulation(bn_model_user_action,
game_state_bn_name, attempt_bn_name,
agent_assistance_bn_name,
agent_policy,
- state_space, action_space,
- epoch=50, run = 50, task_complexity=5, max_attempt_per_object=4, alpha_learning=0):
+ agent_objective,
+ epsilon,
+ state_space,
+ epoch=50, run = 50, task_complexity=5, max_attempt_per_object=4):
'''
Args:
@@ -229,7 +248,7 @@ def simulation(bn_model_user_action,
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)]
+ n_assistance_lev_per_episode_run = [[0 for i in range(Agent_Assistance.counter.value+1)] for j in range(run)]
for r in range(run):
@@ -271,9 +290,9 @@ def simulation(bn_model_user_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)
+ selected_agent_behaviour_action = select_agent_action(query_agent_behaviour_prob.values, agent_objective=agent_objective, epsilon=epsilon)
else:
- selected_agent_behaviour_action = select_agent_action(agent_policy[current_state_index], epsilon=0.2)
+ selected_agent_behaviour_action = select_agent_action(agent_policy[current_state_index], agent_objective=agent_objective, epsilon=epsilon)
#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])
@@ -321,6 +340,7 @@ def simulation(bn_model_user_action,
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))
+ print("Assisttance level for episode:", n_assistance_lev_per_episode_run)
#save episode
episodes.append(Episode(episode))
diff --git a/test.py b/test.py
index b5fd6abccd6355314018406ca22782d58d7840f2..45ffe12564556582cc80883d41d14a207d93610b 100644
--- a/test.py
+++ b/test.py
@@ -82,7 +82,7 @@ print( " Q shared ", q_shared.values)
# })
# print("BEFORE")
# print(q1.values)
-# df = bn.sampling(DAG_update, n=1000)
+# df = bn.sampling(DAG_update, n=1)
# DAG_update = bn.parameter_learning.fit(DAG_update, df)
# q1 = bn.inference.fit(DAG_update, variables=['user_action'], evidence={
# 'game_state': 0,
@@ -92,7 +92,7 @@ print( " Q shared ", q_shared.values)
# print("AFTER")
# print(q1.values)
-#df = bn.sampling(DAG, n=1000, verbose=2)
+#df = bn.sampling(DAG, n=1, verbose=2)
#model = bn.structure_learning.fit(df)
#G = bn.plot(model)
#DAGnew = bn.parameter_learning.fit(model, df, methodtype="bayes")
diff --git a/utils.py b/utils.py
index 41d788bacb6523191606f13cbaea7ba8515ecca6..9366e0f38dfdb0d0a030c390f89af96fb36cc81b 100644
--- a/utils.py
+++ b/utils.py
@@ -5,13 +5,13 @@ import pickle
def plot2D_game_performance(save_path, n_episodes, scaling_factor=1, *y):
# The position of the bars on the x-axis
barWidth = 0.35
- r = np.arange(n_episodes)[1::scaling_factor] # the x locations for the groups
+ r = np.arange(n_episodes) # the x locations for the groups
# Get values from the group and categories
- x = [i for i in range(n_episodes)][1::scaling_factor]
- correct = list(map(lambda x:x[0], y[0]))[1::scaling_factor]
- wrong = list(map(lambda x:x[1], y[0]))[1::scaling_factor]
- timeout = list(map(lambda x:x[2], y[0]))[1::scaling_factor]
- max_attempt = list(map(lambda x:x[3], y[0]))[1::scaling_factor]
+ x = [i for i in range(1, n_episodes+1)]
+ correct = list(map(lambda x:x[0], y[0]))
+ wrong = list(map(lambda x:x[1], y[0]))
+ timeout = list(map(lambda x:x[2], y[0]))
+ max_attempt = list(map(lambda x:x[3], y[0]))
# plot bars
plt.figure(figsize=(10, 7))
@@ -22,28 +22,29 @@ def plot2D_game_performance(save_path, n_episodes, scaling_factor=1, *y):
plt.bar(r, max_attempt, bottom=np.array(correct) + np.array(wrong) + np.array(timeout), edgecolor='white',
width=barWidth, label='max_attempt')
- plt.legend()
+ plt.legend(loc="upper right")
# Custom X axis
plt.xticks(r, x, fontweight='bold')
- plt.ylabel("performance")
+ plt.ylabel("sim patient performance")
+ plt.xlabel("epoch")
plt.savefig(save_path)
- plt.show()
def plot2D_assistance(save_path, n_episodes, scaling_factor=1, *y):
# The position of the bars on the x-axis
barWidth = 0.35
- r = np.arange(n_episodes)[1::scaling_factor]
+ r = np.arange(n_episodes+1)
# the x locations for the groups
# Get values from the group and categories
- x = [i for i in range(n_episodes)][1::scaling_factor]
+ x = [i for i in range(1, n_episodes+2)]
- lev_0 = list(map(lambda x:x[0], y[0]))[1::scaling_factor]
- lev_1 = list(map(lambda x:x[1], y[0]))[1::scaling_factor]
- lev_2 = list(map(lambda x:x[2], y[0]))[1::scaling_factor]
- lev_3 = list(map(lambda x:x[3], y[0]))[1::scaling_factor]
- lev_4 = list(map(lambda x:x[4], y[0]))[1::scaling_factor]
- lev_5 = list(map(lambda x:x[5], y[0]))[1::scaling_factor]
+ lev_0 = list(map(lambda x:x[0], y[0]))
+ lev_1 = list(map(lambda x:x[1], y[0]))
+ lev_2 = list(map(lambda x:x[2], y[0]))
+ lev_3 = list(map(lambda x:x[3], y[0]))
+ lev_4 = list(map(lambda x:x[4], y[0]))
+ lev_5 = list(map(lambda x:x[5], y[0]))
+ lev_6 = list(map(lambda x:x[6], y[0]))
# plot bars
plt.figure(figsize=(10, 7))
@@ -57,23 +58,26 @@ def plot2D_assistance(save_path, n_episodes, scaling_factor=1, *y):
width=barWidth, label='lev_4')
plt.bar(r, lev_5, bottom=np.array(lev_0) + np.array(lev_1) + np.array(lev_2) + np.array(lev_3)+ np.array(lev_4), edgecolor='white',
width=barWidth, label='lev_5')
+ plt.bar(r, lev_6, bottom=np.array(lev_0) + np.array(lev_1) + np.array(lev_2) + np.array(lev_3) + np.array(lev_4)+np.array(lev_5),
+ edgecolor='white',
+ width=barWidth, label='lev_6')
- plt.legend()
+ plt.legend(loc="upper right")
# Custom X axis
plt.xticks(r, x, fontweight='bold')
- plt.ylabel("assistance")
+ plt.ylabel("Levels of assistance")
+ plt.xlabel("Epoch")
plt.savefig(save_path)
- plt.show()
def plot2D_feedback(save_path, n_episodes, scaling_factor=1, *y):
# The position of the bars on the x-axis
barWidth = 0.35
- r = np.arange(n_episodes)[1::scaling_factor] # the x locations for the groups
+ r = np.arange(n_episodes)[1::scaling_factor+1] # the x locations for the groups
# Get values from the group and categories
- x = [i for i in range(n_episodes)][1::scaling_factor]
+ x = [i for i in range(n_episodes)][1::scaling_factor+1]
- feedback_no = list(map(lambda x:x[0], y[0]))[1::scaling_factor]
- feedback_yes = list(map(lambda x:x[1], y[0]))[1::scaling_factor]
+ feedback_no = list(map(lambda x:x[0], y[0]))[0::scaling_factor]
+ feedback_yes = list(map(lambda x:x[1], y[0]))[0::scaling_factor]
# plot bars
plt.figure(figsize=(10, 7))