Intelligence Analysis Based on Intervenient Optimum Learning Guide System
Zengtang Qu, Ping He · 2009
In this paper, we present intervenient optimum learning guide (IOLG), a system for learning non-optimum lean heuristics under resource constraints. IOLG is an implementation of a genetics based learning framework we have developed for improving the performance of intelligence in application problem solvers. Besides providing a flexible and modular framework for conducting experiments, IOLG provides a optimum non-optimum for experimenting with various resource scheduling, generalization, and non-optimum lean strategies, a intervenient optimum learning guide system (IOLGS) that can be easily interfaced to new applications and can be customized based on user requirements and target environments. This paper describes the application independent functions provided by IOLGS, and the application dependent functions for interfacing to new problem solvers. By adjusting various global parameters in IOLGS users can control the numerous options and alternatives in IOLGS.