Epstein Secrets

36.3 Inference Control in PLN 281 36.3.4 Basic Forward and Backward Inference Steps The choice of an inference step, at the microscopic level, may be done in a number of

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

nference Control 277 36.1 Introduction 277 36.2 High-Level Control Mechanisms 277 36.2.1 The Need for Adaptive Inference Control 278 36.3 Inference Control in PLN 279 36.3.1 Representing PLN Rules as GroundedSchemallodes 279 36.3.2 Recording Executed PLN Inferences in the Atomspace 279 36.3.3 A

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

hosen an alternate strategy for PLN. We have one inference control scheme, which is quite simple, but which relies partially on EFTA00624425 36.3 Inference Control in PLN 279 structures coming from outside PLN proper. The requisite variety of inference control strategies is provided by variety in the non-PLN

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