agents = [None,None]
for agent_i in [0,1]:
agents[agent_i] = BisbasModel(
states =
[{"name":"Hunger", "value":1},
{"name":"SocialNeed", "value":1},
{"name":"RomanticNeed","value":1},
{"name":"KnowledgeNeed","value":0.7}],
actions =
UnitModel.GetLayerOfUnits([{"name":"Eat", "value":0},
{"name":"Meet friends", "value":0},
{"name":"Approach potential partner","value":0},
{"name":"Study","value":0}]),
elicitors =
[{"name":"Food", "value":0},
{"name":"Friends", "value":0},
{"name":"Potential partner","value":0},
{"name":"Library","value":0}],
baseline_pos_expectancy=1,
baseline_neg_expectancy=1,
baseline_action_threshold=2,
learning_rate=0.05,
action_tendency_persistence=1-0.08,
satiation_power=0.05,
consummatory_power=0.05,
gains_v_losses=1)
#model doesn't do anything: there's no environment elicitation. let's bring some food in.
for e in agents[agent_i].elicitors:
e.value=1.0
#let's start with a model similiar to the last but with just one extra State, Mood.
bb=BisbasModel(
states =
[{"name":"Hunger", "value":1},
{"name":"SocialNeed", "value":1},
{"name":"RomanticNeed","value":1},
{"name":"KnowledgeNeed","value":0.7}],
actions =
UnitModel.GetLayerOfUnits([
{"name":"Eat", "value":0},
{"name":"Meet friends", "value":0},
{"name":"Approach potential partner","value":0},
{"name":"Study","value":0}]),
elicitors =
[{"name":"Food", "value":0},
{"name":"Friends", "value":0},
{"name":"Potential partner","value":0},
{"name":"Library","value":0}],
baseline_pos_expectancy=1,
baseline_neg_expectancy=1,
baseline_action_threshold=2,
learning_rate=0.05,
action_tendency_persistence=1-0.08,
satiation_power=0.05,
consummatory_power=0.05,
gains_v_losses=0.8)
bb.display_current_state_text()
#keep it simple - map each action to the corresponding state
import numpy as np
import time
import os
import matplotlib.pyplot as plt #we might do some other tool later.
from ActionModel import ActionModel
from BisbasModel import BisbasModel
baseline_pos_expectancy=1
baseline_neg_expectancy=1
action_state_elicitations=10
# actions = [(ActionModel("Action" + str(i),0,0,baseline_pos_expectancy,baseline_neg_expectancy,1,1,1,0))
# for i in (range(1,action_state_elicitations+1))]
bb=BisbasModel.asSimpleModel(action_state_elicitations=4,
baseline_pos_expectancy=1,
baseline_neg_expectancy=1,
baseline_action_threshold=3,
learning_rate=0.1,
action_tendency_persistence=0.9)
#keep it simple - map each action to the corresponding state
bb.action_elicitor = np.identity(len(bb.actions))/10
bb.action_state = np.identity(len(bb.actions))/10
bb.actions[0].name="Eat"
bb.actions[1].name="Study"
bb.actions[2].name="Approach Partner"
bb.actions[3].name="Meet Friends"
#bb.actions[4].name="Be Happy"
#let's make the model hungry...
#There's no explicit Action associated with Mood, but most actions have some effect on it.
#Learning: positive feedback increases mood; negative feedback decreases it. Not sure how to implement this..
#let's start with a model similiar to the last but with just one extra State, Mood.
bb=BisbasModel(
states =
[{"name":"Hunger", "value":1},
{"name":"SocialNeed", "value":1},
{"name":"RomanticNeed","value":1},
{"name":"KnowledgeNeed","value":1},
{"name":"Mood","value":1}],
actions =
UnitModel.GetLayerOfUnits([{"name":"Eat", "value":0},
{"name":"Meet friends", "value":0},
{"name":"Approach potential partner","value":0},
{"name":"Study","value":0}]),
elicitors =
[{"name":"Food", "value":0},
{"name":"Friends", "value":0},
{"name":"Potential partner","value":0},
{"name":"Library","value":0}],
baseline_pos_expectancy=1,
baseline_neg_expectancy=1,
baseline_action_threshold=3,
learning_rate=0.05,
action_tendency_persistence=0.9)
bb.display_current_state_text()
#keep it simple - map each action to the corresponding state
#need to set up the relationships.
import numpy as np
import os
import matplotlib.pyplot as plt #we might do some other tool later.
from ActionModel import ActionModel
import collections as c
import traceback
from BisbasModel import BisbasModel
baseline_pos_expectancy=1
baseline_neg_expectancy=1
action_state_elicitations=10
# actions = [(ActionModel("Action" + str(i),0,0,baseline_pos_expectancy,baseline_neg_expectancy,1,1,1,0))
# for i in (range(1,action_state_elicitations+1))]
bb=BisbasModel(action_state_elicitations=4,
baseline_pos_expectancy=1,
baseline_neg_expectancy=1,
baseline_action_threshold=4,
learning_rate=0.1,
action_tendency_persistence=0.9)
#keep it simple - map each action to the corresponding state
bb.consummatory_power = np.identity(len(bb.actions))/10
bb.actions[0].name="Eat"
bb.actions[1].name="Study"
bb.actions[2].name="Approach Partner"
bb.actions[3].name="Meet Friends"
bb.display_current_state_text()
bb.display_current_state()
#let's make the model hungry...
bb.states[0]=1.0
def setup_base_student_model():
baseline_pos_expectancy=1
baseline_neg_expectancy=1
action_state_elicitations=10
#for this model we're going to try build a model which has an explicit desire to seek positive feedback.
#So we're going to make "mood" an extra Need.
#There's no explicit Elicitor for mood, but various elicitors will elicit it to varying degrees.
#actually, we can't do this under the current model because there's no explicit connection between state and elicitors
#only each of their independent connections to actions.
#Mood competes as a Internal State like everything else...
#There's no explicit Action associated with Mood, but most actions have some effect on it.
#Learning: positive feedback increases mood; negative feedback decreases it. Not sure how to implement this..
#let's start with a model similiar to the last but with just one extra State, Mood.
bb=BisbasModel(
states =
[{"name":"Hunger", "value":1},
{"name":"SocialNeed", "value":1},
{"name":"RomanticNeed","value":1},
{"name":"KnowledgeNeed","value":0.7},
{"name":"Fear","value":0}],
actions =
UnitModel.GetLayerOfUnits([
{"name":"Eat", "value":0},
{"name":"Meet friends", "value":0},
{"name":"Approach potential partner","value":0},
{"name":"Study","value":0},
{"name":"Flee from threat","value":0}]),
elicitors =
[{"name":"Food", "value":0},
{"name":"Friends", "value":0},
{"name":"Potential partner","value":0},
{"name":"Library","value":0},
{"name":"Threat","value":0}],
baseline_pos_expectancy=1,
baseline_neg_expectancy=1,
baseline_action_threshold=2,
learning_rate=0.05,
action_tendency_persistence=1-0.08,
satiation_power=0.05,
consummatory_power=0.05,
gains_v_losses=0.8)
bb.display_current_state_text()
#keep it simple - map each action to the corresponding state
#model doesn't do anything: there's no environment elicitation. let's bring some food in.
bb.elicitors[i_food].value=1.0
bb.actions[i_food].neg_expectancy=0.25
bb.actions[i_food].neg_val=0.25
bb.actions[i_study].neg_expectancy=0.36
bb.actions[i_study].neg_val=0.2
#approaching a potential partner carries a high risk of rejection
#while meeting friends carries very little negative but also lesser positive expected gain
#partner
bb.actions[i_partner].pos_val=2
bb.actions[i_partner].pos_expectancy=2
bb.actions[i_partner].neg_val=2.0
bb.actions[i_partner].neg_expectancy=0.825
#friend
bb.actions[i_friends].pos_val=0.8
bb.actions[i_friends].pos_expectancy=0.8
bb.actions[i_friends].neg_val=0.05
bb.actions[i_friends].neg_expectancy=0.05
bb.actions[i_friends].pos_expectancy=0.8
bb.actions[i_friends].neg_expectancy=0.2
#threat
bb.actions[i_fear_threat].pos_val=4
bb.actions[i_fear_threat].pos_expectancy=4
bb.actions[i_fear_threat].neg_val=0
bb.actions[i_fear_threat].neg_expectancy=0
#sensible consummatory values.
# "approach potential partner" will exhaust the opportunity after one turn.
bb.action_elicitor[i_friends,i_friends]=0
# "meet friends" doesn't really get 'consumed' at all..
bb.action_elicitor[i_partner,i_partner]=1
#studying also doesn't get 'consumed'.
bb.action_elicitor[i_study,i_study]=0
#fleeing will quickly remove the threat.
bb.action_elicitor[i_fear_threat,i_fear_threat]=0.5
#but fear lasts a little longer.
bb.action_state[i_fear_threat,i_fear_threat]=bb.action_state[i_fear_threat,i_fear_threat]*2
#neither is the need satiated very quickly.
bb.action_state[i_study,i_study]=bb.action_state[i_study,i_study]/4
return bb
__author__ = 'benjaminsmith' from BisbasModel import BisbasModel student_model = BisbasModel.asSimpleModel()
