Flexible integration of multiple learning methods into a problem solving architecture

Abstract

One of the key issues in so-called multi-strategy learning systems is the degree of freedom and flexibility with which different learning and inference components can be combined. Most of multi-strategy systems only support fixed, tailored integration of the different modules for a specific domain of problems. We will report here our current research on the Massive Memory Architecture (MMA), an attempt to provide a uniform representation framework for inference and learning components supporting flexible, multiple combination of these components. Rather than a specific combination of learning methods, we are interested in an architecture adaptable to different domains where multiple learning strategies (combinations of learning methods) can be programmed or even learned.