def __init__(self):
SyllogisticReasoningModel.__init__(self)
self.params["dominant_quality"] = "neg"
self.params["dominant_quantifier"] = "some"
self.param_grid["dominant_quality"] = ["neg", "pos"]
self.param_grid["dominant_quantifier"] = ["some", "all"]
def __init__(self):
SyllogisticReasoningModel.__init__(self)
self.params["total_order"] = [["E", "O", "I", "A"], ["=", "=", ">"]]
relations = [["="] * 3, [">"] * 3, ["=", "=", ">"], ["=", ">", "="],
[">", "=", "="], ["=", ">", ">"], [">", "=", ">"],
[">", ">", "="]]
moods = list(itertools.permutations(["A", "E", "I", "O"]))
self.param_grid["total_order"] = list([
(tuple(x), tuple(y))
for x, y in itertools.product(moods, relations)
])
def __init__(self):
SyllogisticReasoningModel.__init__(self)
# For meaning of parameteres see Polk & Newell 1995.
self.params["p1"] = "b"
self.params["p2"] = "a"
self.params["p3"] = "a"
self.params["p4"] = "a"
self.params["p5"] = "b"
self.params["p6"] = "a"
self.params["p10"] = "b"
self.params["p11"] = "b"
self.params["p12"] = "b"
self.params["p13"] = "c"
self.params["p14"] = "b"
self.params["p15"] = "c"
self.params["p16"] = "c"
self.params["p17"] = "c"
self.params["p18"] = "b"
self.params["p19"] = "c"
self.params["p20"] = "b"
self.params["p21"] = "c"
# Commented out parameter ranges are implemented but left out to reduce parameter space.
self.param_grid["p1"] = ["a", "b", "c"] # ["a", "b", "c", "d", "e"]
self.param_grid["p2"] = ["a", "b", "c"] # ["a", "b", "c", "d", "e"]
self.param_grid["p3"] = ["a", "b"]
self.param_grid["p4"] = ["a", "b", "c"] # ["a", "b", "c", "d"]
self.param_grid["p5"] = ["a", "b"]
self.param_grid["p6"] = ["a", "b", "c"] # ["a", "b", "c", "d"]
self.param_grid["p10"] = ["a", "b"] # VR1/2: "a", VR3: "b"
self.param_grid["p11"] = ["a", "b"] # VR1: "a", VR2/3: "b"
self.param_grid["p12"] = ["a", "b"] # VR1: "a", VR2/3: "b"
self.param_grid["p13"] = ["a", "c"] # VR1/2: "a", VR3: "c"
self.param_grid["p14"] = ["a", "b"] # VR1/2: "a", VR3: "b"
self.param_grid["p15"] = ["a", "c"] # VR1/2: "a", VR3: "c"
self.param_grid["p16"] = ["a", "c"] # VR1/2: "a", VR3: "c"
self.param_grid["p17"] = ["a", "c"] # VR1/2: "a", VR3: "c"
self.param_grid["p18"] = ["a", "b"] # VR1/2: "a", VR3: "b"
self.param_grid["p19"] = ["a", "c"] # VR1/2: "a", VR3: "c"
self.param_grid["p20"] = ["a", "b"] # VR1/2: "a", VR3: "b"
self.param_grid["p21"] = ["a", "c"] # VR1/2: "a", VR3: "c"
self.is_stochastic = False
# global counter to measure access time
self.t = -1
def __init__(self):
SyllogisticReasoningModel.__init__(self)
# Use blackbox logically correct reasoning as deduction mechanism
self.reasoning_model = LogicallyValidLookup()
self.params["reverse_first_premise"] = 1.0
self.params["reverse_second_premise"] = 1.0
self.params["reverse_A"] = 1.0
self.params["reverse_O"] = 1.0
self.param_grid["reverse_first_premise"] = [0.0, 1.0]
self.param_grid["reverse_second_premise"] = [0.0, 1.0]
self.param_grid["reverse_A"] = [0.0, 1.0]
self.param_grid["reverse_O"] = [0.0, 1.0]
def __init__(self):
SyllogisticReasoningModel.__init__(self)
self.params["p-entailment"] = 0.6
# max heuristic
self.params["confidenceA"] = 0.7
self.params["confidenceI"] = 0.6
self.params["confidenceE"] = 0.5
self.params["confidenceO"] = 0.4
self.param_grid["p-entailment"] = [0.0, 1.0]
self.param_grid["confidenceA"] = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
self.param_grid["confidenceI"] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
self.param_grid["confidenceE"] = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
self.param_grid["confidenceO"] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
def __init__(self):
SyllogisticReasoningModel.__init__(self)
# Size of encoded models
self.params["lambda"] = 4.0
# Deviation from canoncality in model encoding
self.params["epsilon"] = 0.0
# The probability that counterexamples are searched for (= sigma in Khemlani 2016)
self.params["System 2"] = 1.0
# The probability that a conclusion is weakened when a counterexample is found (rather than returning NVC)
self.params["Weaken"] = 1.0
# Same grid as Khemlani and Johnson-Laird 2016
self.param_grid["lambda"] = [2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0]
self.param_grid["epsilon"] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
self.param_grid["System 2"] = [0.0, 1.0]
self.param_grid["Weaken"] = [0.0, 1.0]
def __init__(self):
SyllogisticReasoningModel.__init__(self)
# Prospensity to guess instead of replying NVC if no conclusion is found
self.params["guess"] = 0.0
# Whether or not existential implicatures are added to the forward propositions
self.params["premise_implicatures_existential"] = True
# Whether or not gricean implicatures are added to the forward propositions
self.params["premise_implicatures_grice"] = True
# Whether or not proving conclusion implicatures is required to prove a conclusion
self.params["conclusion_implicatures"] = False
# Availability of rules
self.params["rule_transitivity"] = True
self.params["rule_exclusivity"] = True
self.params["rule_conversion"] = True
self.params["rule_fw_and_elimination"] = True
self.params["rule_bw_and_introduction"] = True
self.params["rule_bw_conjunctive_syllogism"] = True
self.params["rule_bw_if_elimination"] = True
self.params["rule_bw_not_introduction"] = True
self.param_grid["guess"] = [0.0, 1.0]
self.param_grid["premise_implicatures_existential"] = [True, False]
self.param_grid["premise_implicatures_grice"] = [True, False]
self.param_grid["conclusion_implicatures"] = [False, True]
self.param_grid["rule_transitivity"] = [True, False]
self.param_grid["rule_exclusivity"] = [True, False]
self.param_grid["rule_conversion"] = [True, False]
self.param_grid["rule_fw_and_elimination"] = [True, False]
self.param_grid["rule_bw_and_introduction"] = [True, False]
self.param_grid["rule_bw_conjunctive_syllogism"] = [True, False]
self.param_grid["rule_bw_if_elimination"] = [True, False]
self.param_grid["rule_bw_not_introduction"] = [True, False]
def generate_param_configurations(self):
configs = SyllogisticReasoningModel.generate_param_configurations(self)
configs = [c for c in configs if c["confidenceA"] > c["confidenceI"] and c["confidenceI"] > c["confidenceE"] and c["confidenceE"] > c["confidenceO"]]
return configs
def __init__(self):
SyllogisticReasoningModel.__init__(self)
def __init__(self):
SyllogisticReasoningModel.__init__(self)
self.params["falsify_first"] = 0.5
self.param_grid["falsify_first"] = [0.0, 0.5, 0.95]
self.params["falsify_further"] = 0.5
self.param_grid["falsify_further"] = [0.0, 0.5, 0.95]