Epstein Secrets

subgraphs containing Atom-valued variables . Each such subgraph may be represented as a PredicateNode, and frequent subgraph mining will find such PredicateNodes that have surprisingly high truth values when evaluated across the Atomspace. But unlike MOSES when applied as described above, such an algorithm wi

formation, using the revision rule, and using a default parameter value for the weight of evidence. There is one pragmatic difference here from the PredicateNode case, though. As the system learns new PredicateNodes, its best estimate of pr(C) may change. Thus it makes sense for the system to store the a prio

(ExecutionLink eat:GroundedSchemallode (ListLink $1011619)))) (EvaluationLink increased: PredicateNode (ListLink (EvaluationLink EnergyDemandGoal:PredicateNode))) 37.4.2 The Fishgram Algorithm The core Fishgram algorithm, in pseudocode, is as follows: EFTA00624448 302 37 Pattern Mining initial layer

entioned: 1. Via preprocessing of perceptual inputs (e.g. the names of NumberNode, CharacterNodes) 2. Via hard-wiring of names for Schemallodes and PredicateNodes corresponding to built-in elementary schema (e.g. +, AND, Say) If an Atom A has a name n in the system, we may write A.name n On the other hand,

ables in there. (Sometimes not.) So, we want to have the option to use variables, or not. 20.3.3.2 An Example of Variable Elimination Consider the PredicateNode AND InheritanceLink X cat eats X mice Here we have used a syntactically sugared representation involving the variable X. How can we get rid of t

exts may be used in schema execution, but they're used only indirectly, via being passed to TruthValueEvaluators used for evaluating truth values of PredicateNodes or Com- poundTermNodes that occur internally in schemata being executed. EFTA00624272 EFTA00624273 Section III Perception and Action EFTA00624

le "default thinking" is done, and it is assigned the truth value of the predicate on the given arguments to be the TruthValue of the corresponding PredicateNode (which is defined as the mean truth value of the predicate across all arguments known to CogPrime. ) 25.2.2 Effort Based Procedure Evaluation The

specification PredicateNode as true as possible). In evolutionary computing lingo, the specification predicate is a fitness function. Searching for PredicateNodes that embody patterns in the AtomSpace as a whole is a spe- cial case of this kind of learning, where the specification PredicateNode embodies a notio

here do these properties P come from? These properties should be patterns learned as part of the probabilistic modeling of the Combo tree inside the PredicateNode at position x. For example, if one is using a decision tree modeling framework, then the properties might be of the form decision tree D evaluates

. • Imitation Learning: Learning via copying what some other agent is observed to do. • Implication: Often refers to an ImplicationLink between two PredicateNodes, indicating an (extensional, intensional or mixed) logical implication. • Implicit Knowledge Representation: Representation of knowledge via having

cognitive dynamics, especially ECAN. But then the MapEncapstdation MindAgent may do its pattern mining and recognize this map explicitly, and form a PredicateNode encapsulating it. This PredicateNode may then be used in PLN inference, conceptual blending, and so forth (e.g. helping with the formation of a con

. • Imitation Learning: Learning via copying what some other agent is observed to do. • Implication: Often refers to an ImplicationLink between two PredicateNodes, indicating an (extensional, intensional or mixed) logical implication. • Implicit Knowledge Representation: Representation of knowledge via having

ge over some fuzzy set of goals and environments. • Predicate Evaluation: The process of determining the Truth Value of a predicate, embod- ied in a PredicateNode. This may be recursive, as the predicate referenced internally by a Grounded PredicateNode (and represented via a Combo program tree) may itself int

er atoms. Grounded- PredicateNodes and GroundedSchemaNodes connect to explicitly represented procedures (sometimes in the Combo language); ungrounded PredicateNodes and SchemaNodes are abstract and, like ConceptNodes, purely characterized by their relationships. Node Probability: Many PLN inference rules rely on

ge over some fuzzy set of goals and environments. e Predicate Evaluation: The process of determining the Truth Value of a predicate, embod- ied in a PredicateNode. This may be recursive, as the predicate referenced internally by a Grounded PredicateNode (and represented via a Combo program tree) may itself inte