Agential AI for integrated continual learning, deliberative behavior, and comprehensible models

Authors: Zeki Doruk Erden, Boi Faltings
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: continual learning, deliberative behavior, planning, comprehensibility, agent

Abstract:
Contemporary machine learning paradigm excels in statistical data analysis, solving problems that classical AI couldn't. However, it faces key limitations, such as lack of integration with planning, incomprehensible internal structures, and inability to learn continually without erasing prior knowledge. We present initial design for an AI system, Agential AI (AAI), in principle operating independently or on top of statistical methods, that overcomes all 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 encapsulate high-level behavior patterns. Preliminary experiments on a simple abstract environment show AAI's effectiveness and future potential.

Decision: reject

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