Cumulative Learning: Towards Designing Cognitive Architectures for Artificial Agents that Have a Lifetime

Cognitive architectures should be designed with learning performance as a central goal. A critical feature of intelligence is the ability to apply the knowledge learned in one context to a new context. A cognitive agent is expected to have a lifetime, in which it has to learn to solve several different types of tasks in its environment. In such a situation, the agent should become increasingly better adapted to its environment. This means that its learning performance on each new task should improve as it is able to transfer knowledge learned in previous tasks to the solution of the new task. We call this ability cumulative learning. Cumulative learning thus refers to the accumulation of learned knowledge over a lifetime, and its application to the learning of new tasks. We believe that creating agents that exhibit sophisticated, long-term, adaptive behavior is going to require this kind of approach.