Epstein Secrets

The Laptop is Safe from the Rain 273 35.5.3 Fetching the Toy Inside the Upper Cupboard 273 35.6 An Integrative Approach to Planning 275 36 Adaptive, Integrative Inference Control 277 36.1 Introduction 277 36.2 High-Level Control Mechanisms 277 36.2.1 The Need for Adaptive Inference Contr

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,

of the neighborhood of partial plans, and to guide PLN's inferences regarding the success likelihood of partial plans. EFTA00624423 Chapter 36 Adaptive, Integrative Inference Control 36.1 Introduction The subtlest and most difficult aspect of logical inference is not the logical rule-set nor the m

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

each rule with probability proportional to a certain weight associated with each rule. Initially we can assign these weights EFTA00624428 282 36 Adaptive, Integrative Inference Control generically, by hand, separately for each application domain. Later on they should be chosen adaptively, based on in

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