Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models

Zeki Doruk Erden (École Polytechnique Fédérale de Lausanne), Boi Faltings (École Polytechnique Fédérale de Lausanne)

Abstract

Contemporary machine learning faces key limitations, such as a lack of integration with planning, incomprehensible structure, and inability to learn continually. We present initial design for system, Agential AI (AAI), that overcomes these issues. AAI's core is a learning method that models temporal dynamics with guarantees of completeness, minimality, and continual learning. It integrates this with a behavior algorithm that plans on a learned model and encapsulates high-level behavior patterns. Preliminary experiments on a simple environment show AAI's effectiveness and potential. 1