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]