  Agents of general intelligence deployed in real-world scenarios must adapt to
ever-changing environmental conditions. While such adaptive agents may leverage
engineered knowledge, they will require the capacity to construct and evaluate
knowledge themselves from their own experience in a bottom-up, constructivist
fashion. This position paper builds on the idea of encoding knowledge as
temporally extended predictions through the use of general value functions.
Prior work has focused on learning predictions about externally derived signals
about a task or environment (e.g. battery level, joint position). Here we
advocate that the agent should also predict internally generated signals
regarding its own learning process - for example, an agent's confidence in its
learned predictions. Finally, we suggest how such information would be
beneficial in creating an introspective agent that is able to learn to make
good decisions in a complex, changing world.
