Goal-driven learning in the GILA integrated intelligence architecture
Jainarayan Radhakrishnan, Santiago Ontañón, Ashwin Ram · 2009
Goal Driven Learning (GDL) focuses on systems that determine by themselves what has to be learnt and how to learn it. Typically GDL systems use meta-reasoning capabilities over a base reasoner, identifying learning goals and devising strategies. In this paper we present a novel GDL technique to deal with complex AI systems where the meta-reasoning module has to analyze the reasoning trace of multiple components with potentially dif-ferent learning paradigms. Our approach works by distributing the generation of learning strategies among the different modules instead of centraliz-ing it in the meta-reasoner. We implemented our technique in the GILA system, that works in the airspace task orders domain, showing an increase in performance. 1