An important feature of human learning is the ability to continuously accept new information and unify it with existing knowledge, a process that proceeds largely automatically and without catastrophic side-effects. A generally intelligent machine (AGI) should be able to learn a wide range of tasks in a variety of environments. Knowledge acquisition in partially-known and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing knowledge while learning new things, increasing the scope of ability and knowledge incrementally—without catastrophic forgetting or damaging existing skills. Many aspects of such learning have been addressed in artificial intelligence (AI) research, but relatively few examples of cumulative learning have been demonstrated to date and no generally accepted explicit definition exists of this category of learning. Here we provide a general definition of cumulative learning and describe how it relates to other concepts frequently used in the AI literature.

A generally intelligent machine (AGI) should be able to learn a wide range of tasks. Knowledge acquisition in complex and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing knowledge during learning, supporting increases in the scope of ability and knowledge, incrementally and predictably — without catastrophic forgetting or mangling of existing knowledge. Where relevant expertise is at hand the learning process can be aided by curriculum-based teaching, where a teacher divides a high-level task up into smaller and simpler pieces and presents them in an order that facilitates learning. Creating such a curriculum can benefit from expert knowledge of (a) the task domain, (b) the learning system itself, and (c) general teaching principles. Curriculum design for AI systems has so far been rather ad-hoc and limited to systems incapable of cumulative learning. We present a task analysis methodology that utilizes expert knowledge and is intended to inform the construction of teaching curricula for cumulative learners. Inspired in part by methods from knowledge engineering and functional requirements analysis, our strategy decomposes high-level tasks in three ways based on involved actions, features and functionality. We show how this methodology can be used for a (simplified) arrival control task from the air traffic control domain, where extensive expert knowledge is available and teaching cumulative learners is required to facilitate the safe and trustworthy automation of complex workflows.