Epstein Secrets

y, there may be patterns across large numbers of nodes that merit encapsulation in individual nodes. For instance, if there are 1000 fairly similar ConceptNodes, it may be better not to merge them all together, but rather to create a single node to which they all link, reifying the category that they collect

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

han using a Handle based notation which Atoms would be referred to as 1, 3433322, etc.; but sometimes we will use the Handle notation as well. Some ConceptNodes and conceptual PredicateNode or Schemallodes may correspond with human-language words or phrases like cat, bite, and so forth. This will be the mino

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

MemberLinks. This is in accordance with the notion of glocal memory, in the language of which the HPAN attractors are the maps and the corresponding ConceptNodes are the keys. Put simply, when a HPAN attractor is recognized, MemberLinks are created between the HPAN nodes comprising the main body of the attra

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

usual, but making use of Metallodes as well as ordinary CT nodes. The modeling of Metallodes is quite similar to the modeling of Nodes representing ConceptNodes and PredicateNodes using embedding vectors. In this way, one can use standard, small decision tree models to model fairly large portions of CTs (be

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

at contains all required packages and tools to test and develop OpenCog. • Concept Creation: A general term for cognitive processes that create new ConceptNodes, PredicateNodes or concept maps representing new concepts. • Conceptual Blending: A process of creating new concepts via judiciously combining pie

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

at contains all required packages and tools to test and develop OpenCog. • Concept Creation: A general term for cognitive processes that create new ConceptNodes, PredicateNodes or concept maps representing new concepts. • Conceptual Blending: A process of creating new concepts via judiciously combining pie

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

hat contains all required packages and tools to test and develop OpenCog. e Concept Creation: A general term for cognitive processes that create new ConceptNodes, PredicateNodes or concept maps representing new concepts. e Conceptual Blending: A process of creating new concepts via judiciously combining piece

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