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#!/usr/bin/env python3
import pyactr as actr
import prod_numbers
Model = actr.ACTRModel()
DM = Model.decmem
goal = Model.goal
imaginal = Model.set_goal(name="imaginal", delay=0.2)
_ = prod_numbers.number(Model)
def get_digit(number, n):
return number // 10**n % 10
actr.chunktype("number", ("number", "next", "ones", "tens", "hundreds"))
actr.chunktype(
"math_goal",
(
"task",
"result1",
"result2",
"op",
"nextop",
"nextnextop",
"arg1",
"arg1_idx",
"arg2",
"arg2_idx",
"result",
"expand_slot",
"hundreds1",
"tens1",
"ones1",
"hundreds2",
"tens2",
"ones2",
),
)
actr.chunktype(
"math_op",
(
"op",
"arg1",
"arg2",
"result",
),
)
# Add numbers 0-999 to decmem
for i in range(0, 1000):
DM.add(
actr.makechunk(
f"number{str(i)}",
"number",
number=i,
next=i + 1,
ones=get_digit(i, 0),
tens=get_digit(i, 1),
hundreds=get_digit(i, 2),
)
)
Model.productionstring(
name="continue_with_next_op",
string="""
=g>
isa math_goal
op done
nextop ~None
nextop =nextop
nextnextop =nextnextop
==>
=g>
isa math_goal
op =nextop
nextop =nextnextop
nextnextop None
""",
)
Model.productionstring(
name="reset",
string="""
=g>
isa math_goal
op reset
==>
=g>
isa math_goal
op expand
nextop reset
"""
)
goal.add(
actr.makechunk(
"",
"math_goal",
op="expand",
nextop="reset",
expand_slot=None,
ones1=1,
tens1=2,
hundreds1=3,
ones2=4,
tens2=5,
hundreds2=6,
)
)
Model.model_parameters["activation_trace"] = True
Model.model_parameters["instantaneous_noise"] = .2
Model.model_parameters["utility_alpha"] = 0.5
Model.model_parameters["utility_noise"] = 0.5
Model.model_parameters["production_compilation"] = True
Model.model_parameters["utility_learning"] = True
sim = Model.simulation(
gui=False,
)
sim.run(max_time=2)
|
