Mark Ring. Recurrent Transition Hierarchies for
Continual Learning: A general overview. AAAI Workshops, San
Francisco, August, 2011.
Continual learning is the unending process of learning new
things on top of what has already been learned (Ring, 1994).
Temporal Transition Hierarchies (TTHs) were developed to allow
prediction of Markov-k
sequences in a way that was consistent with the needs of a
continual-learning agent (Ring, 1993). However, the algorithm could
not learn arbitrary temporal contingencies. This paper describes
Recurrent Transition Hierarchies (RTH), a learning method that
combines several properties desirable for agents that must learn as
they go. In particular, it learns online and incrementally,
autonomously discovering new features as learning progresses. It
requires no reset or episodes. It has a simple learning rule with
update complexity linear in the number of parameters.