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

Agent migration, and machines joining or leaving the CogPrime cluster. So, at the Unit level, attention allocation in CogPrime has two aspects: how MindAgents and Tasks receive attention from CogPrime, and how Atoms receive attention from different MindAgents and Tasks. The topic of this Section is the fo

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

to Atoms with more impor- tance, and hence creates an evolutionary, dynamic with importance as the fitness criterion and the whole constellation of MindAgents as the novelty-generation mechanism. However, MOSES explicitly embodies evolutionary dynamics for the learning of new patterns and procedures that

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

links that could simply be found by the pattern matcher when trying to find variable assignments to satisfy (El, E2, E3, E4}. 36.4.1 Breakdown into MindAgents To make this sort of PLN dynamic work, we require a number of MindAgents to be operating "ambiently" in the background whenever inference is occurr